The evolution of senescence through decelerating selection for system reliability
Bibliographic record
Abstract
Senescence is a universal phenomenon in organisms, characterized by increasing mortality and decreasing fecundity with advancing chronological age. Most proximate agents of senescence, such as reactive oxygen species and UV radiation, are thought to operate by causing a gradual build-up of bodily damage. Yet most current evolutionary theories of senescence emphasize the deleterious effects of functioning genes in late life, leaving a gap between proximate and ultimate explanations. Here, we present an evolutionary model of senescence based on reliability theory, in which beneficial genes or gene products gradually get damaged and thereby fail, rather than actively cause harm. Specifically, the model allows organisms to evolve multiple redundant copies of a gene product (or gene) that performs a vital function, assuming that organisms can avoid condition-dependent death so long as at least one copy remains undamaged. We show that organisms with low levels of extrinsic mortality, and high levels of genetic damage, tend to evolve high levels of redundancy, and that mutation-selection balance results in a stable population distribution of the number of redundant elements. In contrast to previous evolutionary models of senescence, the mortality curves that emerge from such populations match empirical senescence patterns in three key respects: they exhibit: (1) an initially low, but rapidly increasing mortality rate at young ages, (2) a plateau in mortality at advanced ages and (3) 'mortality compensation', whereby the height of the mortality plateau is independent of the environmental conditions under which different populations evolved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".