Species with a chemical defence, but not chemical offence, live longer
Bibliographic record
Abstract
Evolutionary hypotheses for ageing generally predict that delayed senescence should evolve in organisms that experience lower extrinsic mortality. Thus, one might expect species that are highly toxic or venomous (i.e. chemically protected) will have longer lifespans than related species that are not likewise protected. This remarkable relationship has been suggested to occur in amphibians and snakes. First, we show that chemical protection is highly conserved in several lineages of amphibians and snakes. Therefore, accounting for phylogenetic autocorrelation is critical when conservatively testing evolutionary hypotheses because species may possess similar longevities and defensive attributes simply through shared ancestry. Herein, we compare maximum longevity of chemically protected and nonprotected species, controlling for potential nonindependence of traits among species using recently available phylogenies. Our analyses confirm that longevity is positively correlated with body size in both groups which is consistent with life-history theory. We also show that maximum lifespan was positively associated with chemical protection in amphibian species but not in snakes. Chemical protection is defensive in amphibians, but primarily offensive (involved in prey capture) in snakes. Thus, we find that although chemical defence in amphibians favours long life, there is no evidence that chemical offence in snakes does the same.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".