ANTAGONISTIC PLEIOTROPY, MORTALITY SOURCE INTERACTIONS, AND THE EVOLUTIONARY THEORY OF SENESCENCE
Bibliographic record
Abstract
Most theoretical work on the evolution of senescence has assumed that all individuals within a population are equally susceptible to extrinsic sources of mortality. An influential qualitative prediction based on this assumption is Williams's hypothesis, which states that more rapid senescence is expected to evolve when the magnitude of such extrinsic mortality sources is increased. Much evidence suggests, however, that for many groups of organisms externally imposed mortality risk is a function of an organism's internal condition and hence susceptibility to such hazards. Here we use a model of antagonistic pleiotropy to investigate the consequences that such interactions (between environmental hazard and internal condition) can have for Williams's hypothesis. As with some previous theory examining noninteractive extrinsic mortality sources, we find that an increase in interactive extrinsic sources of mortality makes it less likely that an individual will survive from birth to any given age, weakening selection against physiological deterioration at all ages and thus favoring more rapid senescence. However, an increase in interactive mortality sources also typically strengthens selection against physiological deterioration at any age, given an individual has survived to that age, because it reduces the fitness of poor-condition individuals more than good-condition individuals. These opposing effects are not felt equally at all ages, with the latter predominating at early ages. The combined effects can therefore result in the novel prediction that an increase in interactive extrinsic mortality sources can select for slower senescent deterioration early in life but more rapid deterioration late in life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".