Signs, Science, and the Scientist Who Couldn't Tell Them Apart

An entertaining journey into how confusing a sign with science can change the fate of mankind.

Imagine a scientist who has spent thirty years studying the natural world. He has published papers, attended conferences, delivered lectures, and supervised doctoral students. There is just one small problem: he cannot distinguish between the words signs and science. To make matters worse, he believes they mean more or less the same thing.

When asked about the importance of science for the betterment of mankind, he adjusts his spectacles, clears his throat, and begins.

“Science,” he announces confidently, “is the ability to recognise signs. A black cat crossing your path is a sign. Rain on a wedding day is a sign. A student getting low marks is a sign of a poor future. And if a person sneezes just as you are about to leave home, well, that is clearly a sign that the universe wants you to stay indoors!”

The audience looks puzzled. The scientist, however, is delighted with his explanation. He has found signs everywhere. In fact, he has found so many signs that he no longer needs to investigate anything.

And that, dear reader, is precisely where our story becomes interesting.

The ancient art of confusing signs with science

Human beings have always searched for signs. We look at the clouds to predict rain, examine animal behaviour to anticipate danger, and interpret patterns in the world around us. This ability to notice patterns can be useful. Sometimes, what appears to be a simple sign is the first clue to a genuine discovery.

Dark clouds may indeed indicate approaching rain. A fever may indicate an infection. A sudden decline in a bird population may indicate an ecological disturbance.

But there is a rather important distinction between noticing a sign and understanding what it signifies.

A fever does not tell you which pathogen is responsible. A declining bird population does not tell you whether habitat loss, pesticides, disease, or climate change is the cause. Dark clouds do not tell you precisely when, where, or how much it will rain.

A sign may suggest a possibility. Science investigates whether that possibility is true, why it might be true, and under what conditions it remains true.

Our scientist, unfortunately, has skipped this second step. He sees a correlation and declares a cause, notices a coincidence and announces a law of nature, and mistakes his interpretation of an observation for an established fact.

His laboratory is full of signs. His understanding of the world, however, remains remarkably empty.

When signs become substitutes for explanations

Let us imagine that our scientist is invited to investigate why some people live longer than others.

He observes that several long-lived individuals were born on particular days of the week. He concludes that the day of birth determines longevity.

When a colleague asks whether diet, genetics, healthcare, income, or living conditions might explain the pattern, he waves the questions away.

“Those are details,” he says. “I have identified the sign!”

But has he?

To establish whether the day of birth has any relationship with longevity, he would need reliable data, an appropriate sample, statistical analysis, consideration of confounding factors, and independent replication. Even if a statistical association emerged, he would still need to investigate whether it was causal or merely coincidental.

The fact that two events occur together does not establish that one causes the other. A pattern observed in a small sample may disappear in a larger one. An apparently convincing explanation may fail when tested against new evidence.

Science is not simply the collection of observations that support what we already believe. It is a systematic process for testing explanations, including those we find most attractive.

And perhaps its most uncomfortable lesson is this: the world is under no obligation to behave according to our expectations.

Science: humanity's most powerful error-correction system

Now let us imagine that our scientist is transported back several centuries, when infectious diseases were poorly understood.

He observes that people sometimes recover after a ritual, a prayer, a particular treatment, or the passage of time. He concludes that whichever event occurred immediately before recovery must have caused it.

Had this approach governed medicine, we might still be relying on anecdotes to decide which treatments work.

Modern medicine developed through many contributions, including observation, experimentation, clinical trials, microbiology, epidemiology, and the critical evaluation of previous beliefs. Not every medical intervention works, and even widely used treatments must be evaluated for their benefits and harms.

The same scientific approach has transformed agriculture, sanitation, transportation, communication, and our understanding of the universe.

Consider a few examples:

• Vaccination: Scientific investigation has helped establish how vaccines stimulate immune protection and how their effectiveness and safety can be assessed.

• Agriculture: Genetics, plant breeding, soil science, and crop disease research have helped increase agricultural productivity and manage threats to food security.

• Clean water: Microbiology, chemistry, and public health research have established the importance of water treatment and sanitation in preventing disease.

• Communication: Advances in physics, materials science, electronics, and information theory underpin technologies that connect people across the world.

• Conservation: Ecology, evolutionary biology, and population genetics help us understand biodiversity loss and design evidence-informed conservation strategies.

These achievements did not arise merely because someone noticed a sign. They required people to ask questions, develop hypotheses, collect evidence, challenge assumptions, and improve explanations.

Science does not promise that every investigation will succeed. It offers something more valuable: a disciplined way to learn from failure and distinguish reliable knowledge from unsupported belief.

But isn't science also about recognising signs?

Our scientist, having listened to these arguments, raises an objection.

“Surely science begins with signs! A scientist observes something unusual, recognises a pattern, and investigates it. Why should I be criticised for noticing signs?”

For once, he has made a good point.

Science certainly begins with observations. A fossil, an unusual genetic sequence, a strange astronomical signal, or an unexpected experimental result may provide the clue that launches an entire research programme.

The problem is not recognising signs. The problem is assuming that recognising a sign is equivalent to explaining it.

Imagine finding the remains of an unfamiliar animal. Its teeth might suggest a particular diet. Its limbs might suggest a particular mode of locomotion. Its DNA might reveal evolutionary relationships.

Each observation provides evidence. But each inference must be evaluated in light of alternative explanations, the limitations of the data, and additional evidence.

A good scientist asks:

1. What exactly have I observed?

2. What explanations could account for this observation?

3. What evidence would distinguish those explanations?

4. What result would convince me that my preferred explanation is wrong?

5. Can other investigators reproduce or independently verify the finding?

The last question is especially important. Science is not simply an individual scientist's confidence in an idea. It is a collective enterprise in which claims are exposed to criticism and evidence is made available for scrutiny.

Our scientist is beginning to understand. Signs may open the door to scientific discovery, but they cannot replace the work required to walk through it.

The danger of believing that every sign has a hidden meaning

There is another difficulty with our scientist's approach: if every event is treated as a sign, almost any outcome can be used to support almost any belief.

Suppose he predicts that it will rain tomorrow because he saw a crow sitting on a particular tree.

If it rains, he declares his prediction successful.

If it does not rain, he says the crow was warning him that rain had been prevented by an unknown force.

If the crow flies away, he claims the sign has changed.

His belief survives every possible outcome because he has devised an explanation for each one.

But a claim that accommodates every outcome without risk of being contradicted tells us very little about the world.

Scientific explanations, by contrast, should generate testable expectations. A useful hypothesis tells us what observations would support it and, crucially, what observations would count against it.

This does not mean every scientific question can be settled by one simple experiment. Historical sciences such as evolutionary biology, geology, and astronomy often test explanations by comparing multiple independent lines of evidence. Nor does it mean that every scientific idea must be immediately falsifiable in a simplistic sense.

It means that scientific claims must be answerable to evidence rather than protected from it.

A scientist who never allows evidence to change their mind is not practising the central discipline that makes science valuable.

Science does not make humans infallible

At this point, we must be careful not to make the opposite mistake.

Science has not eliminated human error. Scientists can be biased, institutions can fail, studies can be poorly designed, and published conclusions can later prove incorrect. Research can also be misused, and technological progress does not automatically guarantee moral progress.

Science can tell us what consequences a particular action is likely to have, but it cannot independently decide every question about what society ought to value.

For example, scientific research can estimate the environmental consequences of a proposed development, its effects on public health, and the trade-offs among different technologies. Decisions about fairness, rights, acceptable risks, and the distribution of benefits also involve ethical and political judgments.

Science is indispensable for understanding the consequences of our choices, but human judgment remains essential in deciding what choices to make.

This is why scientific literacy matters beyond the laboratory. Citizens need to distinguish established findings from preliminary evidence, recognise uncertainty, evaluate claims made by authorities, and understand the difference between a plausible story and a well-supported explanation.

Science is not the blind worship of experts. It is an invitation to examine how knowledge is produced and how strongly the available evidence supports a conclusion.

A final lesson for our confused scientist

Our scientist now sits quietly in his laboratory. The black cat has crossed his path again.

He looks at it, thinks for a moment, and resists the temptation to announce that it predicts either good fortune or disaster.

Instead, he asks whether black cats behave differently from cats of other colours in some measurable situation. He wonders whether there is a biological mechanism worth investigating. He considers how he might collect data, control for other variables, and test his hypothesis.

Then he pauses.

Perhaps there is no interesting scientific question here at all.

And that, too, is progress.

A scientist need not turn every observation into a discovery. Sometimes the correct conclusion is that the available evidence is insufficient, the apparent pattern is coincidental, or the question is not worth pursuing.

Our scientist has finally learned that the world is full of signs, but their meanings are not always what we imagine. To understand them, we need more than curiosity. We need evidence, reasoning, humility, and a willingness to be wrong.

He opens his notebook and writes a new definition:

Signs are clues we notice. Science is the disciplined process by which we investigate what those clues can actually tell us.

He underlines it twice.

Then he checks the spelling of science.

Just to be safe.

The moral of the story

Humanity does not progress merely by finding patterns in nature. It progresses by learning which patterns are real, which explanations withstand scrutiny, and which beliefs must be abandoned when the evidence demands it.

Science has helped us combat disease, produce food, understand evolution, explore space, and improve living conditions. Its contribution is not that it provides an answer to every question, but that it gives us methods for asking better questions and correcting our mistakes.

The distinction between signs and science may appear trivial to someone who confuses their spellings. The distinction between interpreting a sign and testing an explanation, however, can determine whether we discover a new treatment or perpetuate a harmful superstition.

And perhaps the most important lesson is this:

The purpose of science is not to make the world conform to our beliefs. It is to make our beliefs conform, as reliably as possible, to the world.

When AI Audits Evolutionary Genomics: Deep Review Takes on a 261-Genome Gene-Loss Study

What happens when an AI reviewer is given a published evolutionary-genomics paper built from hundreds of genomes, multiple orthology pipelines, raw sequencing reads, phylogenetics, synteny, selection analyses, and comparative statistics?

We recently put our 2026 BMC Biology paper, “Chromosomal rearrangements and segmental deletions contribute to gene loss in squamates,” through Google Deep Review.

The study screened 261 squamate genome assemblies and combined genome annotations, synteny, BLAST searches, raw short- and long-read data, transcriptomes, pairwise genome alignments, paralog phylogenetics and comparative genomic analyses to investigate genes apparently missing from lizards and snakes.

Deep Review was broadly positive about the scientific ambition of the study. It highlighted the scale of the genomic survey, the multi-evidence validation framework, and particularly the detailed IL34-CSF1R case study as strengths.

But then it produced an unusually detailed list of criticisms.

Some are straightforward typographical errors.

Some are surprisingly sharp internal-consistency checks.

Some identify genuine statistical questions.

And, as with our previous experience using Deep Review, a few take a reasonable concern and push it further than the evidence allows.

So once again, the useful question is not:

“Was Deep Review right?”

It is:

“Which parts of the review survive a review of the review?”

First, what was the paper actually claiming?

This distinction turns out to be central.

The study began with 53 genes whose orthologs could not be retrieved from squamate genomes. It then attempted to distinguish genuine evolutionary loss from annotation failure, assembly problems, sequence divergence and paralog confusion.

The final classification reported:

14 genes: ortholog loss with no retained paralog
15 genes: ortholog loss with retained paralogs
24 genes: unresolved or unretrieved orthologs

Thus, the strongest evolutionary-loss inference applies to 29 genes, while another 24 remain deliberately unresolved.

That hierarchy of evidence is scientifically important.

Deep Review's first major criticism was that the paper occasionally forgets its own hierarchy.

And on that point, it is right.

Auditing the criticisms one by one

Deep Review criticism	Assessment	My evaluation
The Abstract sometimes frames all 53 unretrieved orthologs as gene losses, although only 29 are classified as supported losses.	✅ Valid and important	The Abstract first refers to evidence for the “putative loss of 53 genes,” but immediately later divides them into 14 + 15 supported losses and 24 unretrieved orthologs. The latter wording is the scientifically precise interpretation. “53 candidate unretrieved orthologs, of which 29 have stronger evidence for loss” would be clearer.
Five genes occur simultaneously in the retained-paralog loss category and the unresolved category.	✅ Unambiguously valid	This is an excellent catch. FREM2, LCP1, LRCH1, SIAH3, and STOML3 appear in the 15-gene supported retained-paralog category and then reappear immediately in the “remaining 24” unresolved set. The published text confirms this duplication.
The “remaining 24” paragraph actually contains only 23 names and omits six low-identity genes.	✅ Valid	Reconstructing the lists confirms Deep Review's arithmetic. The omitted genes are DNM3, HPGDS, MATK, MOB1A, RBBP7, and ZNF438. Replacing the five duplicated names with these six restores the intended 24 unique genes and the total 14 + 15 + 24 = 53. This is exactly the kind of tedious bookkeeping at which automated review excels.
The manuscript says 19 genes show segmental deletion but lists 20.	✅ Valid	The sentence says “19 out of 36 genes” but the enumerated list contains 20 genes. This is a straightforward numerical error.
The Discussion refers to 14 genes lost with retained paralogs although the reported category contains 15.	✅ Valid	The paper repeatedly reports 15 retained-paralog losses, but page 9 calls the category 14. Another simple consistency error.
Some Figure 2A structural classifications conflict with the textual gene classifications.	✅/⚠️ Strong concern requiring a source-table audit	Figure 2A separates 36 segmental-deletion cases and 17 rearrangement cases, yet several genes described as rearrangement-driven in the text appear under the segmental-deletion panel and vice versa. The discrepancy is visible enough to merit correction, although the safest fix is to regenerate both prose and figure directly from one definitive classification table rather than adjudicate individual cases manually.
Text says 8 intrachromosomal + 9 interchromosomal rearrangements, whereas Figure 2 appears to indicate 10 + 7.	✅ Likely valid	The published text explicitly gives 8 and 9. If Figure 2's set-size bars encode 10 and 7, one representation must be updated.
Figure 3 appears to contradict the statement that 14 genes lack paralogs.	⚠️ Partly valid, but the review needs nuance	Figure 3 shows human Ensembl paralogs, whereas “no retained paralog” appears to refer to paralogs recovered and retained in the relevant squamate orthology analysis. Those are not necessarily the same statement. Deep Review is right that the terminology is confusing, but this need not be a biological contradiction. The manuscript should distinguish “has a paralog in the vertebrate/human gene family” from “has a retained squamate paralog capable of complicating ortholog-loss inference.”
Sequence-identity thresholds shift between 55%, 65% and 70%.	✅/⚠️ Valid reporting problem, but not necessarily one universal cutoff	Category (i) uses >55% as part of one decision rule, the unresolved set is discussed using <65%, and the Discussion later describes many genes as <70%. These thresholds may have different descriptive roles, but that distinction is not clear enough. Deep Review is right that the classification algorithm should be stated formally.
Coverage boundaries change between ≤25%, 25–74%, ≥75% and >75%.	✅ Valid minor inconsistency	This is largely boundary notation rather than a scientific flaw, but exact thresholds matter in a classification pipeline.
The phylogenetic logistic regression treats a single ancestral loss as though many squamate species provide independent evidence.	✅ The most important statistical criticism	IL34 loss appears to have occurred once on the ancestral squamate branch. Descendant squamate species are therefore repeated observations of one evolutionary transition rather than dozens of independent loss events. The comparative-methods literature explicitly warns that when a discrete character has only one evolutionary origin, even hundreds of sampled species cannot provide replicated evolutionary tests of that trait.
Replacing the logistic model with PGLS would solve the problem.	⚠️ Only partly	A PGLS model treating relative intergenic distance as the continuous response could be more natural for quantifying lineage-associated genomic contraction. But PGLS cannot create independent evolutionary origins where only one exists. The statistical limitation should therefore be acknowledged even after changing models.
The Wilcoxon analyses also risk phylogenetic pseudoreplication.	✅ Valid caution	Pooling species observations assumes more independence than the phylogeny actually supplies. This matters especially when all squamates inherit a shared ancestral genomic event. It does not mean the observed contraction is unreal, only that the nominal p-value may overstate the number of independent evolutionary observations.
The 6 × 6 intergenic-distance ratio matrix is insufficiently defined.	✅ Valid, and confirmed by the public code	The released R script calculates every ordered ratio of six pairwise genomic distances and performs one-tailed Wilcoxon tests. Because A/B and B/A have opposite interpretations, numerator and denominator order must be stated explicitly. Deep Review correctly identified a reproducibility gap here.
TOGA should have been considered as an orthogonal loss-validation framework.	⚠️ Good suggestion, not an error	TOGA integrates gene annotation and orthology inference from whole-genome alignments and is specifically capable of detecting gene losses. It would be a particularly valuable additional check for the 24 unresolved genes. But the absence of TOGA does not invalidate a pipeline already combining synteny, raw reads, genome alignments, HybPiper and phylogenetics.
BLASTn alone may lack sensitivity across ~250–280 million years of divergence.	✅ Valid methodological concern	The Methods do not specify the BLASTn task. This matters because NCBI recommends megablast for highly similar sequences, whereas traditional blastn or discontiguous megablast are more suitable for interspecies comparisons and use substantially shorter seeds. Translated searches or alignment-chain approaches would provide useful safeguards against false negatives in highly divergent genes.
The tBLASTx workflow is mentioned in Results but insufficiently described in Methods.	✅ Valid	If translated searches contribute to the evidence score, parameters and scope should be reproducible.
Cross-species RNA-seq mapping with minimap2 -ax splice -uf -k14 may generate false negatives.	✅/⚠️ Valid concern, dependent on library design	Minimap2 documentation states that -uf forces alignment to the forward transcript strand and is appropriate for genuinely stranded direct-RNA or Iso-Seq data. It warns that using it for data that are not appropriately stranded can cause errors. Mapping Pogona RNA reads directly to chicken across deep divergence further reduces sensitivity. Deep Review is right to request clarification, but this does not prove that all negative RNA evidence is invalid.
Custom LASTZ alignments lack sufficient parameter detail.	✅ Valid reproducibility criticism	For seven newly generated whole-genome alignments, software version, masking strategy, scoring, seeding, chaining and netting should ideally be documented.
Exon overlap divided by CDS length could inflate values if UTRs or overlapping alignment blocks are included.	⚠️ Valid clarification request, not a demonstrated error	The Methods should state whether only CDS features were used and whether overlapping alignment blocks were merged. Without inspecting the exact intermediate files, Deep Review cannot establish that inflation actually occurred.
The paper incorrectly uses the RELAX parameter k when discussing codeml.	✅ Strongly valid	This is an important methodological wording error. HyPhy RELAX specifically estimates the selection-intensity parameter k, where significant k < 1 indicates relaxation and k > 1 intensification. PAML codeml instead models ω = dN/dS and branch-specific ω values. The two frameworks cannot be described using the same k criterion.
FEL and MEME species-specific analyses are under-described.	✅/⚠️ Valid request for clarification	The paper depicts site-level results for focal species, but the exact branch sets, significance thresholds and procedure used to generate species-specific FEL/MEME profiles should be specified. Deep Review is right to request this. Whether the analysis itself is invalid cannot be determined from the manuscript alone.
TNIP2 is called unresolved in one section and subsequently treated as a confirmed loss.	✅ Unambiguously valid	Page 10 explicitly says TNIP2 remains an unretrieved ortholog because true loss cannot yet be distinguished from methodological limitations. Page 12 then states that the study “reports the loss” of IL34, STAP1, LAPTM5, and TNIP2. Those two statements cannot both stand without qualification.
RBBP7 and FREM2 are also discussed functionally as though their loss is established.	✅ Valid	Discussing their biological roles is entirely reasonable, but unresolved candidates should remain labelled as unresolved candidates throughout the biological interpretation.
Retention and transcription of a paralog do not demonstrate functional compensation.	✅ Valid and conceptually important	Figure 8 provides good evidence that CAV3-like exists and is transcribed. That establishes a prerequisite for compensation, not compensation itself. Demonstrating compensation would require expression shifts in relevant tissues, altered selection, functional assays or other evidence.
Paralog buffering is not automatically evidence for the “less-is-more” hypothesis.	✅ Valid conceptual distinction	Adaptive gene loss and neutral loss permitted by functional redundancy are different evolutionary scenarios. If a pre-existing paralog simply makes one duplicate dispensable, this can fit birth-and-death gene-family evolution without requiring adaptive loss. The paper should present compensation and adaptive advantage as hypotheses rather than equivalents.
Using mouse knockout phenotypes to conclude that squamates are immunologically compromised is too strong.	✅ Strongly valid	This is perhaps Deep Review's best biological-interpretation criticism. The paper states that squamates may be “functionally compromised” and more susceptible to pathogens because IL34 is absent. But squamates have evolved without IL34 for hundreds of millions of years, while the study itself reports possible compensatory evolution of CSF1R. Mouse knockouts reveal what IL34 does in mice, not the immune competence of an ancient reptilian lineage.
The pharmaceutical extrapolation is too speculative.	✅ Valid	A comparative-genomics study can motivate hypotheses about immune-system remodeling. Claiming that these findings may directly enable new macrophage-modulating pharmaceutical strategies moves several experimental steps beyond the presented evidence.
The Conclusion says all unretrieved genes are GC-neutral and members of multigene families.	✅ Valid overgeneralization	The paper itself reports that 14 lack retained paralogs and that 19 genes exceed 55% GC. What the data more convincingly show is that these genes do not resemble the extreme GC-rich “dark genes” responsible for many historical avian assembly failures. “Most show moderate rather than extreme GC content” would be more precise than universally “GC-neutral.”
The cumulative evidence score is not mathematically defined.	✅ Important reproducibility issue	Figure 2 ranks genes by a cumulative evidence score, but the Methods do not clearly state the weighting formula and exact decision logic translating individual evidence layers into the three final categories. This should be explicit in a computational genomics paper.
The GC-stretch metric is shown but not adequately defined.	✅ Valid	If a quantity appears in Figure 2B and contributes to interpretation, its computation should be reproducible.
Upstream and downstream distance labels are swapped in the Methods.	✅ Valid but computationally harmless	The left and right flanks are labelled opposite to standard upstream/downstream terminology. Because those quantities are added in the denominator, the swap does not change the calculated relative-distance value. This is a nomenclature problem, not a numerical one.
Supplementary Figure S5 calls TNIP2 intrachromosomal although the main text describes an interchromosomal rearrangement.	✅ Valid	The public repository itself contains a directory explicitly named TNIP2-Interchromosomal_rearrangement, strengthening the interpretation that the Supplementary Information description is the incorrect one.
Reference numbering, duplicate references and corrupted bibliographic entries require correction.	✅ Valid	These include duplicate platypus references, incorrect citations for HyPhy/PAML, a missing journal title, a corrupted GUIDANCE2 citation, duplicated journal/year strings, “Science (80-)”, and “Nielsent R.” Deep Review's reference audit is impressively thorough.
Numerous small figure-label and gene-name errors.	✅/⚠️ Mostly valid	Errors such as “Closet paralog,” “LAPTM5S,” “EVOVL3,” “Coelacanthimormes,” aBSERL, and inconsistent IL34 naming are straightforward. Suggestions such as changing IONP2 to LONP2 or ADAP1L to ADAP1 should still be checked against the underlying annotation rather than accepted automatically.

The biggest thing Deep Review found was not a typo

The duplicated gene names are eye-catching, but the deeper issue is the distinction between 53 unretrieved genes and 29 reasonably supported gene losses.

That distinction already exists in the paper.

The problem is that the wording sometimes collapses it.

This matters because “we could not retrieve an ortholog” and “the lineage lost the gene” are fundamentally different statements.

A gene can become difficult to retrieve because of:

high sequence divergence,

assembly gaps,

annotation failure,

rapid evolution,

duplication followed by paralog confusion,

chromosomal relocation,

or genuine deletion.

The entire purpose of the paper's multi-evidence framework was to distinguish these possibilities.

Therefore, the strongest wording should preserve its own hierarchy:

53 initial unretrieved orthologs → 29 stronger gene-loss candidates → 24 unresolved candidates.

Deep Review is correct that treating all 53 interchangeably as “lost genes” weakens one of the paper's conceptual strengths.

This is a good example of AI review detecting something that is not a flaw in the underlying experiment, but a flaw in how the experiment tells its own story.

The category-counting errors are almost tailor-made for Deep Review

The most striking part of this report is its reconstruction of the gene lists.

A human reviewer reads:

14 + 15 + 24 = 53.

Everything looks fine.

Deep Review reads the actual names.

It notices that five genes occur in both mutually exclusive categories.

It counts the “24” names and finds 23.

It then compares the list against the earlier 24-gene low-identity set and identifies exactly six names that disappeared.

That is not sophisticated evolutionary theory.

It is relentless bookkeeping.

And that is precisely why it is useful.

Scientific papers contain hundreds of little relational constraints:

this table must equal that paragraph,

this gene must occur in one category,

this figure must contain the same number as the Methods,

this p-value must refer to the same test described elsewhere.

Humans are excellent at understanding the biological narrative. We are not equally good at remembering every noun encountered six pages earlier.

Deep Review has a different weakness profile.

It does not get tired.

The phylogenetic logistic regression criticism is more serious

The IL34 analysis is biologically compelling.

The gene is retained in outgroups.

Its syntenic interval contracts in squamates.

Long PacBio and Nanopore reads span the expected locus without revealing an assembly gap.

BLAST searches fail to recover the gene.

The surrounding genes remain.

There is additional evolutionary signal in its receptor, CSF1R.

That remains a strong comparative-genomic case.

But Deep Review raises a legitimate question about the reported phylogenetic logistic regression.

The paper assigns:

squamates = 0
non-squamates = 1

and asks whether relative intergenic distance predicts this status across 179 species.

The issue is evolutionary replication.

If IL34 disappeared once in the common ancestor of living squamates, then a gecko, cobra, python, anole and bearded dragon are not five independent gene-loss experiments.

They inherited one ancestral event.

Recent reviews of phylogenetic comparative methods make this point starkly: a discrete trait with a single evolutionary origin cannot support an ordinary replicated test of that trait's evolution merely by sampling hundreds of descendant species.

This does not weaken the physical evidence for the deletion.

It weakens the interpretation of the very small regression p-value as an independent statistical confirmation of that deletion.

The regression is best viewed as describing a powerful clade-associated association, not 179 replicated evolutionary tests.

Would PGLS solve everything?

No.

This is where Deep Review itself deserves reviewing.

Its suggestion to model intergenic distance using PGLS is sensible. A continuous genomic-distance response is arguably more natural than predicting gene status from distance.

But PGLS cannot manufacture independent gene-loss origins.

If the question is:

“Did independent evolutionary losses repeatedly accompany reduced distance?”

the dataset contains essentially one squamate loss event.

If the question is:

“Is the squamate lineage characterized by a phylogenetically structured contraction of this interval?”

then a phylogenetic continuous-trait model is more appropriate.

Those are different questions.

Deep Review spots the problem correctly, but its proposed remedy is not a magic eraser.

Deep Review is also right about BLAST sensitivity

Another important criticism concerns the search strategy.

The study is deliberately conservative about highly divergent genes. It explicitly leaves 24 genes unresolved partly because nucleotide divergence could make them hard to retrieve.

That is good.

But the Methods do not specify the blastn task used.

That detail matters.

NCBI documentation distinguishes megablast, optimized for very similar nucleotide sequences, from traditional blastn and discontiguous megablast, which are more appropriate for interspecies comparisons. The default seed sizes differ substantially.

Across roughly 250 to 280 million years of divergence, a highly divergent coding gene may become invisible to a relatively strict nucleotide search while remaining clearly detectable at the amino-acid or syntenic level.

So Deep Review's suggestion to use or discuss translated searches and TOGA is scientifically sensible.

TOGA is particularly relevant because it integrates structural gene annotation with orthology inference from whole-genome alignments and explicitly detects gene-loss events.

But this is where wording matters again.

“TOGA would strengthen the analysis” is justified.

“The analysis is invalid without TOGA” would not be.

The study already uses far more evidence than a simple NCBI annotation screen, including long reads, synteny, raw sequencing data, HybPiper, IQ-TREE and genome alignments.

TOGA would be an excellent independent judge, particularly for the 24 unresolved defendants.

The RNA-seq criticism is technically sharp

Deep Review also examines this command:

minimap2 -ax splice -uf -k14

and asks whether -uf was appropriate.

This is not nitpicking.

The minimap2 documentation says -uf forces mapping to the forward transcript strand. It is specifically recommended for stranded direct-RNA and suitable Iso-Seq data, and the documentation warns that using it on reads without the expected orientation can increase errors.

So if the cross-species datasets include unstranded or differently stranded libraries, clarification is needed.

There is an additional biological issue.

The analysis maps RNA-seq from Pogona vitticeps to the chicken genome across enormous evolutionary divergence.

A lack of mapping in such an experiment is not equivalent to absence of transcription.

This makes negative RNA evidence weaker than positive RNA evidence.

That does not erase the gene-loss signal when long-read genomic and syntenic evidence independently support deletion.

But it should affect the weight assigned to transcriptomic non-detection.

Again, Deep Review is most useful when it forces each piece of evidence to carry exactly the weight it deserves.

The RELAX versus codeml criticism is definitely correct

This is another excellent technical catch.

The Methods state that RELAX and codeml were used to investigate relaxed selection and then describe:

k > 1 = intensified selection
k < 1 = relaxed selection

That description belongs specifically to HyPhy RELAX.

RELAX estimates the selection-intensity parameter k. Its documentation explicitly states that significant k > 1 indicates intensified selection and significant k < 1 indicates relaxation.

PAML codeml does something different.

Its codon models estimate ω = dN/dS, including branch-specific ω values.

An increased foreground ω can arise from relaxed purifying selection, positive selection, or a mixture of processes depending on the model and context.

So Deep Review is right.

The Methods should specify separately:

what RELAX tested,

what codeml models were compared,

what parameter was examined,

which likelihood-ratio tests were performed,

and how significance was corrected across multiple foreground branches.

This is not merely grammar. It is methodological interpretation.

But Deep Review occasionally asks for more than the study can provide

One place where the review becomes somewhat demanding is its treatment of paralog compensation.

The paper says genes such as CAV3, KDM3A, ADAP2, SUV39H2, and TBC1D14 could be compensated by retained paralogs.

That conditional word matters.

Figure 8 demonstrates that a CAV3-like paralog is retained and transcribed in Pogona.

Deep Review correctly points out that this does not demonstrate functional compensation.

That is true.

But neither does the manuscript claim a biochemical rescue experiment was performed.

The reasonable revision is therefore not to remove the hypothesis.

It is to state it more explicitly as a hypothesis:

Retention and expression of paralogs provide a plausible substrate for functional buffering, but direct compensation remains to be experimentally demonstrated.

That distinction preserves the evolutionary idea without upgrading correlation into mechanism.

The “less-is-more” criticism is conceptually useful

There is also an elegant evolutionary point hiding in this review.

Suppose gene A and gene B are partially redundant.

Gene A disappears.

Gene B continues performing enough of the shared function that the organism suffers little cost.

Was losing gene A adaptive?

Not necessarily.

Its loss may simply have become selectively tolerable because redundancy reduced the cost.

That is classic birth-and-death gene-family evolution.

The stronger “less-is-more” hypothesis argues that losing a gene can itself be advantageous under changed selective conditions.

Those are different claims.

Deep Review is therefore right that the study should distinguish:

paralog buffering permits gene loss

from

gene loss itself was adaptive.

The first can occur without the second.

Evolution has many ways to throw away a spare key without deciding that keylessness is beneficial.

The immune-compromise claim deserves to be softened

This is the biological criticism I agree with most strongly.

The paper discusses mouse knockout studies showing impaired immune phenotypes after loss of IL34 and then suggests that squamates may be functionally compromised and potentially more susceptible to pathogens.

That extrapolation is too direct.

An acute knockout in a laboratory mouse is not equivalent to an ancient gene loss followed by roughly a quarter-billion years of evolutionary accommodation.

Squamates have had enormous evolutionary time for:

changes in CSF1R,

altered CSF1 signaling,

changes in myeloid development,

regulatory compensation,

paralog recruitment,

or completely different immune-network architecture.

Indeed, the paper's own CSF1R analysis suggests evolutionary modification after IL34 loss.

So a better conclusion is:

The absence of IL34 indicates substantial evolutionary remodeling of the macrophage regulatory network in squamates and raises the question of how alternative pathways compensate for its ancestral function.

That is arguably more interesting than declaring squamates compromised.

A lineage that has successfully produced more than ten thousand living species after losing a gene 250 million years ago may have rewritten the circuitry rather than limped along with a broken component.

The pharmaceutical claim travels even further

The paper concludes that these findings may help develop novel pharmaceutical approaches for monocyte and macrophage immune modulation.

Deep Review objects.

I think correctly.

The evolutionary result may eventually inspire biomedical hypotheses.

But between:

“squamates evolved without IL34”

and

“this informs a therapeutic macrophage-modulation strategy”

lie several missing bridges:

mechanism,

cell biology,

functional compensation,

receptor signaling,

experimental perturbation,

and translational validation.

The safest and stronger scientific framing is that squamates provide a natural evolutionary experiment for studying long-term remodeling of the IL34-CSF1-CSF1R axis.

That is already a compelling result.

There is no need to bolt a pharmaceutical rocket onto it.

Deep Review finds another genuine contradiction in TNIP2

This is a small sentence-level inconsistency with large interpretive consequences.

Page 10 explicitly says TNIP2 remains an unretrieved ortholog because distinguishing true evolutionary loss from methodological limitations remains difficult.

Two pages later:

the study reports the loss of four genes, including TNIP2.

Those are different confidence levels.

Deep Review correctly catches this.

The same principle applies to RBBP7 and FREM2.

It is perfectly legitimate to discuss what the loss of an unresolved gene would mean.

But scientific grammar should preserve uncertainty:

“candidate loss,”

“unretrieved ortholog,”

“potential absence,”

and

“confirmed/high-confidence loss”

should not slowly melt into synonyms as the paper approaches the Discussion.

The GC criticism is mostly about language

The paper's central point regarding GC is reasonable.

Previous apparent gene losses in birds often turned out to be artifacts involving extremely GC-rich regions that were difficult to assemble.

The squamate candidates do not show that same extreme pattern, and high-quality assemblies plus long-read evidence reduce the likelihood of a similar systematic artifact.

But calling all 53 genes “GC-neutral” is stronger than necessary.

Nineteen genes exceed 55% GC, and the observed means extend to about 64.7%.

A more accurate conclusion would be:

The candidate genes do not show the extreme GC enrichment characteristic of several previously reported avian “missing gene” artifacts.

That states exactly what the comparison demonstrates.

Deep Review is right to push for that precision.

The public repository makes several criticisms much easier to judge

This is another repeat of what we learned from the LCR-paper audit.

Open code does not merely allow reproduction of results.

It allows reproduction of criticism.

For the IL34 distance analysis, the public R code confirms that the heatmap is built by calculating ordered ratios among six genomic distances and applying one-tailed Wilcoxon tests.

That means Deep Review's request to explicitly specify numerator and denominator orientation is justified.

The repository also explicitly contains a TNIP2-Interchromosomal_rearrangement analysis directory, which supports Deep Review's suspicion that the Supplementary Information's description of the event as “intrachromosomal” is likely a labeling error.

Open science creates an unusual but healthy situation:

the paper can be audited,

the AI reviewer can be audited,

and then the audit of the AI can itself be audited.

Scientific criticism becomes executable. 🧬

Where Deep Review slightly overreaches

Despite an impressive performance, I would not accept every line of this review without qualification.

Three examples stand out.

First, Figure 3 versus “no retained paralog.”

Deep Review treats these as directly contradictory. But the figure refers to human/Ensembl paralog annotations, whereas the evolutionary classification concerns whether an appropriate paralog is retained in squamates after orthology analysis. This needs clearer terminology, but the two statements are not necessarily biologically incompatible.

Second, PGLS as the solution to the single-transition problem.

Deep Review correctly recognizes phylogenetic pseudoreplication. But PGLS cannot turn one ancestral loss into multiple independent losses. It can improve the modeling of continuous genomic distance, not solve the fundamental lack of replicated loss events.

Third, TOGA.

TOGA would be a valuable additional orthology check, especially for the 24 unresolved genes. But calling its absence a weakness of the existing evidence risks hindsight bias. A multi-evidence pipeline can be rigorous without including every available orthology program.

This distinction between:

“this would improve the paper”

and

“the paper is wrong because it lacks this”

is one that AI reviewers still do not always handle gracefully.

This review strongly resembles Deep Review's broader behavior

Google describes Deep Review as an agentic system designed specifically to catch long-range logical dependencies, mathematical inconsistencies, unsupported claims and factual problems across complex documents. The system grew out of the Paper Assistant Tool used on more than 16,000 conference submissions.

The published PAT evaluation is especially informative.

At STOC and ICML:

92.7% and 90.7% of respondents respectively rated the feedback very or mostly helpful.

But only 55.8% and 64.8% regarded the feedback as mostly or completely grounded.

And 35.4% of ICML respondents said PAT found substantive theoretical gaps, while 31% ran new experiments because of its review.

Those numbers perfectly capture the experience here.

The review is extremely useful without being uniformly correct.

Google's own PAT paper explicitly acknowledges that hallucinations and mistaken criticisms remain a limitation and that humans must verify the AI's findings.

That is exactly the right model.

Compared with our previous Deep Review, this one has a different personality

Our earlier Deep Review of the low-complexity-region benchmarking paper was particularly strong at:

figure-to-text contradictions,

algorithm labels,

coordinate conventions,

Jaccard values,

and computational definitions.

This review is strongest at something slightly different:

scientific bookkeeping across a complex evolutionary argument.

It reconstructs gene sets across pages.

It remembers which genes were unresolved.

It notices when the Discussion silently promotes them to confirmed losses.

It tracks thresholds across Results and Methods.

It checks that 19 really means 19.

It compares the meaning of RELAX with the meaning of codeml.

It distinguishes human paralog annotations from loss classifications.

It asks whether 179 species really mean 179 evolutionary observations.

That is not merely proofreading.

It is closer to database integrity checking applied to scientific prose.

What should actually be corrected?

If I were preparing a formal correction, revised version or future follow-up based on this Deep Review, I would prioritize five things.

First, rebuild the 53-gene master classification table and automatically generate every gene list, count, category and figure label from that single source. That would remove the duplicate/omitted-gene problem at its root.

Second, clearly separate 53 unretrieved orthologs, 29 supported losses, and 24 unresolved candidates everywhere, especially in the Abstract, Discussion and Conclusions.

Third, revise the IL34 statistical language. The physical and genomic evidence for IL34 loss is strong; the phylogenetic logistic regression should be presented as supporting association rather than an independent replicated test of gene-loss evolution.

Fourth, clarify the search and evolutionary-modeling Methods: BLAST task, translated searches, RNA-seq strandedness, custom LASTZ parameters, evidence-score computation, RELAX versus codeml, FEL/MEME branch definitions and multiple-testing procedures.

Fifth, soften the biological extrapolations from “functional compromise” and pharmaceutical relevance toward “evolutionary remodeling, compensatory mechanisms and experimentally testable hypotheses.”

Those changes would strengthen the paper without changing its central comparative-genomic contribution.

Does Deep Review undermine the central result?

I do not think so.

The most compelling individual case, IL34, does not depend on one p-value or one annotation database.

It is supported by multiple convergent observations:

the gene is conserved outside squamates,

the expected syntenic locus is identifiable,

long reads span the locus,

the interval is contracted,

the gene cannot be recovered from genomic searches,

and its receptor shows unusual evolutionary signatures.

Even if one removed the phylogenetic logistic regression completely, that evidence would remain.

Likewise, the broad observation that squamate unretrieved orthologs occur across macro- and microchromosomes and are frequently associated with rearranged genomic regions remains interesting.

What Deep Review does challenge is the precision of the classification boundaries and the strength of some downstream interpretations.

Those are important distinctions.

A paper can contain real discoveries and also contain errors.

Those propositions are not opposites.

Perhaps this is where AI review is genuinely transformative

A conventional human reviewer may spend most of their attention asking:

Is this gene-loss story biologically plausible?

Are 261 genomes enough?

Is IL34 interesting?

Are the figures convincing?

Does the work advance the field?

Those are exactly the questions humans should ask.

Deep Review is unusually willing to ask a different set:

You said 19. Why are there 20 names?

Where did these six genes go?

Why is FREM2 in two mutually exclusive categories?

Why does page 10 say TNIP2 is unresolved while page 12 says it is lost?

Why is RELAX's k parameter being applied to codeml?

Why is a single ancestral loss being represented by 179 tip observations?

Why does the Supplementary Information call an event intrachromosomal when the main text calls it interchromosomal?

None of those questions is glamorous.

Collectively, however, they tighten the bolts holding the scientific argument together.

The verdict

This is another high-value Deep Review.

I would classify its major criticisms roughly as follows:

Most of the internal-consistency findings are fully valid.

The 53-versus-29 distinction is scientifically important.

The category duplication and missing-gene bookkeeping errors are demonstrably real.

The RELAX/codeml issue is technically correct.

The phylogenetic single-transition criticism is substantive and should be acknowledged.

The concerns about BLAST sensitivity and cross-species RNA mapping are well founded but do not prove false negatives occurred.

TOGA is a valuable additional analysis, not a prerequisite that the original study failed to meet.

Paralog compensation is a plausible hypothesis, not yet a demonstrated mechanism.

The claims of immune compromise and pharmaceutical relevance are too strong for the current evidence.

And several of the smaller comments are exactly what they appear to be: typos, mislabeled figures and bibliography debris that survived human review and publication.

That last point is perhaps the most fascinating.

This is already a peer-reviewed paper in BMC Biology.

It went through authors, co-authors, reviewers, editors, copyediting and publication.

Deep Review still found a surprisingly large collection of internally checkable errors.

That does not mean human peer review failed.

It means human peer review and machine review appear to have different error spectra.

Humans remain better positioned to judge biological significance, novelty, plausibility and conceptual importance.

AI is becoming exceptionally good at checking whether every number, name, threshold and logical dependency survives a 19-page journey intact.

The two capabilities are complementary.

The lesson from two papers

After examining Deep Review on two of our published papers, a pattern is beginning to emerge.

Deep Review is at its best when the question can be phrased as:

“Does statement A agree with statement B somewhere else in the document or with an externally verifiable technical definition?”

It is less reliable when the question becomes:

“Is this unconventional scientific choice actually wrong, or merely debatable?”

That boundary is important.

A duplicated gene in two mutually exclusive categories is an error.

Calling one threshold 65% and another 70% may be an error, or it may reflect two different analytical purposes that were inadequately explained.

A k parameter attributed to codeml is an error.

Not using TOGA is a methodological choice.

Calling TNIP2 both unresolved and lost is an error.

Whether paralog retention supports adaptive “less-is-more” evolution is an evolutionary interpretation.

An AI reviewer should have enormous authority over the first kind of question.

It should have considerably less authority over the second.

And that may be the most useful way to integrate these systems into science.

Use AI aggressively to find inconsistencies. Use scientists to decide what those inconsistencies mean.

Deep Review is proving remarkably good at finding the loose threads.

We should still decide which ones actually unravel the sweater.

Signs, Science, and the Scientist Who Couldn't Tell Them Apart

An imaginary scientist, a linguistic confusion and a rather serious lesson about the importance of science for humanity.

Imagine a scientist who has spent decades studying the natural world. He has published papers, attended conferences, supervised doctoral students and acquired a formidable reputation in his field. There is just one small problem: he cannot distinguish between signs and science. To make matters worse, he believes the two words mean essentially the same thing.

When asked about the distinction, he confidently explains that science is the art of interpreting signs, and that the purpose of scientific research is to identify signs that reveal the hidden intentions of the universe.

At first, this sounds amusing. After all, English has plenty of confusing words. But suppose our scientist takes this confusion seriously. Suppose he begins treating every coincidence as evidence, every unusual event as a message and every unexplained phenomenon as a sign that confirms what he already believes.

Suddenly, the confusion becomes considerably more consequential.

The world of signs

Signs are everywhere. Dark clouds may be a sign of approaching rain. A fever may be a sign of infection. Smoke may be a sign of fire. A decline in a bird population may be a sign of environmental degradation.

Human beings are remarkably good at recognising patterns and interpreting signs. Indeed, this ability has been essential to our survival. Our ancestors learned to associate animal tracks with predators, changes in vegetation with seasonal transitions and certain sounds with impending danger.

But recognising a sign is not the same as understanding what caused it.

Dark clouds do not necessarily mean it will rain at a particular location. A fever does not identify a specific pathogen. A declining bird population does not, by itself, establish whether the cause is habitat destruction, pesticides, disease or climate change.

A sign is an observation that may carry information. Its interpretation requires evidence, context and, often, further investigation.

This is where science enters the picture.

The world of science

Science begins with curiosity, but it does not end with curiosity. It is a systematic way of investigating the natural world, testing explanations and correcting errors.

A scientist observing dark clouds might ask several questions. What atmospheric conditions produced them? How much moisture is present? What are the wind patterns? How likely is precipitation? The scientist collects observations, develops hypotheses, examines evidence and evaluates predictions.

The essential distinction is that science does not merely ask what something might signify. It asks what can be demonstrated, what alternative explanations exist and what observations would prove a proposed explanation wrong.

This distinction is fundamental. Anyone can identify a pattern. The scientific challenge is to establish whether that pattern is real, whether it is reproducible and whether the proposed explanation is justified.

Our imaginary scientist, unfortunately, has a different approach. If it rains after he sees a black cat, he records the event as confirmation. If it doesn't rain, he considers it an exception. Eventually, he develops an elaborate theory of meteorology based on feline movements.

His theory may be internally consistent. It may even have an impressive vocabulary. But it has one fatal weakness: it has no reliable connection to the actual mechanisms of rainfall.

When signs replace science

The human mind has a remarkable capacity to find patterns, including patterns that do not exist. We associate events, remember coincidences and construct narratives that make the world feel predictable.

Consider a person who believes that a particular ritual brings good fortune. They perform the ritual and subsequently receive a promotion. The sequence of events may be interpreted as a sign that the ritual worked.

But what happened to all the people who performed the ritual and did not receive a promotion? What about those who received promotions without performing it? What role did qualifications, opportunities, institutional decisions and chance play?

Without comparisons, measurements and appropriate controls, a coincidence can easily be mistaken for causation.

This is precisely why science places so much emphasis on experimental design, statistical reasoning, reproducibility and independent verification.

The problem is not that signs are useless. The problem arises when an observation is mistaken for an explanation, or when an interpretation is protected from scrutiny.

Science is not the rejection of intuition. It is a means of testing intuition.

Science is not just about discoveries

Our scientist is now beginning to understand the difference between the two words. However, he makes another mistake. He assumes that science is important only when it produces spectacular discoveries: a new species, a revolutionary technology, a cure for a previously incurable disease or a theory that transforms our understanding of the universe.

These achievements are certainly important. But science also has an equally valuable, less glamorous responsibility: establishing when we are wrong.

Imagine a physician who discovers that a widely accepted treatment is ineffective. The discovery might not make headlines, produce a new medicine or win a prize. Yet preventing thousands of patients from receiving an ineffective treatment could be an extraordinary contribution to humanity.

Similarly, an ecologist demonstrating that a seemingly beneficial conservation intervention has unintended consequences may prevent considerable environmental damage. A geneticist discovering that an apparent association is caused by a technical artefact may save other researchers years of misguided investigation.

Science is valuable not only because of what it discovers, but also because of what it prevents us from mistakenly believing.

The ability to correct an error is one of the most consequential achievements of scientific thinking.

How science has changed human civilisation

From understanding the invisible to transforming everyday life

Medicine and public health

Understanding microorganisms, developing vaccines, discovering antibiotics and improving sanitation have transformed human survival. These advances emerged from investigations into mechanisms of disease, rather than interpretations of symptoms alone.

Agriculture and food security

Plant genetics, soil science, microbiology and agricultural engineering have helped increase food production, improve crop resilience and understand the ecological costs of agricultural practices.

Energy and engineering

Discoveries in physics, chemistry and materials science have enabled electricity generation, telecommunications, modern transport and increasingly efficient renewable energy systems.

Understanding our planet

Climate science, geology, ecology and satellite observations help us understand natural hazards, monitor environmental change and make better-informed decisions about resource management.

None of these advances depended simply on observing signs. They required investigating underlying processes, developing explanations, testing predictions and accumulating reliable knowledge.

And yet, science alone does not guarantee that its discoveries will benefit humanity. The development of a technology does not determine how it will be used. Nuclear physics, for example, has contributed both to nuclear power and to nuclear weapons.

The betterment of humanity therefore requires scientific knowledge alongside ethical judgement, responsible governance and thoughtful social choices.

Science is a process, not a collection of beliefs

Our scientist now faces a more difficult question. If science is so powerful, does that mean scientists are always right?

Certainly not.

Scientists are human beings. They have assumptions, ambitions, biases, professional incentives and intellectual blind spots. Scientific publications can contain errors. Experiments can be poorly designed. Statistical analyses can be inappropriate. Entire scientific communities can sometimes accept explanations that later prove inadequate.

What distinguishes science is not the perfection of scientists but the availability of methods for identifying and correcting mistakes.

A scientific explanation must remain open to examination. Evidence can strengthen it, contradict it or reveal the need for revision. An explanation that cannot be meaningfully challenged is not made scientific simply because someone with a scientific title supports it.

This is also why disagreement is not inherently a weakness in science. Competing hypotheses can be examined against evidence. Interpretations can be challenged. Independent researchers can attempt to reproduce findings.

Scientific consensus, when supported by substantial convergent evidence, is meaningful. But it is not equivalent to unquestionable authority.

Science is a cumulative enterprise in which today's knowledge becomes the starting point for tomorrow's questions.

The real danger of confusing signs with science

Let us return to our scientist.

He has now learnt to pronounce both words correctly. Unfortunately, he still confuses their meanings.

Whenever he encounters a complex problem, he searches for a convenient explanation. He prefers evidence that confirms his existing ideas and dismisses observations that contradict them. He mistakes confidence for certainty, authority for evidence and correlation for causation.

He may even publish papers, supervise students and participate in important academic discussions.

His difficulty is no longer linguistic. It is epistemological: he has misunderstood how knowledge is established.

And this is not merely a fictional problem confined to laboratories. The same confusion can influence decisions about public health, education, agriculture, environmental protection and public policy.

When people mistake anecdotal experiences for general evidence, unsupported claims can spread. When decision-makers interpret correlations as causal relationships, resources can be wasted. When inconvenient evidence is dismissed rather than investigated, preventable mistakes can persist.

Scientific literacy, therefore, is not simply about knowing scientific facts. It is about being able to distinguish observations from interpretations, evidence from assertions and uncertainty from ignorance.

The importance of asking better questions

Perhaps the greatest contribution of science is not any single discovery, but a particular intellectual habit: the willingness to ask questions whose answers might contradict our expectations.

Why did this happen? How do we know? What else could explain it? What evidence would change our minds? Can someone else independently obtain the same result?

These questions are useful far beyond the natural sciences. They help us evaluate claims, recognise uncertainty, make informed decisions and resist the temptation to accept convenient explanations.

Importantly, science does not answer every question that matters to humanity. It can investigate the consequences of an action, but deciding what is morally right also requires ethical reasoning. It can measure economic outcomes, but determining what constitutes a just society involves values and public deliberation.

Recognising the boundaries of science is itself an essential part of scientific thinking.

A final lesson for our scientist

After years of confusing signs with science, our scientist finally attends a lecture on scientific methodology.

The speaker begins with a simple statement:

"A sign tells you that something may be happening. Science helps you investigate what is happening, why it is happening and how confident you should be in your explanation."

For the first time, the scientist realises that the distinction between the two words is much more than a spelling exercise.

Signs may inspire questions. Science provides a disciplined approach to investigating them.

Signs may alert us to danger. Science helps us identify its causes and develop interventions.

Signs may suggest patterns. Science helps us establish whether those patterns are genuine, meaningful and reproducible.

And while signs can be interpreted in countless ways, scientific explanations are constrained by evidence.

Signs help us notice the world. Science helps us understand it.

And understanding the world gives humanity a better opportunity to change it for the better.

Perhaps, then, the most important thing our scientist needs to learn is not how to spell science. It is how to practise it.

Because humanity does not progress simply by noticing more signs, collecting more observations or accumulating more information. It progresses when we develop better explanations, test them rigorously, acknowledge our mistakes and use the knowledge we gain responsibly.

That is the enduring importance of science: not that it gives us all the answers, but that it offers us a systematic way to find better ones.