Assessing senescence patterns in populations of large mammals
Bibliographic record
Abstract
Theoretical models such as those of Gompertz and Weibull are commonly used to study senescence in survival for humans and laboratory or captive animals. For wild populations of vertebrates, senescence in survival has more commonly been assessed by fitting simple linear or quadratic relationships between survival and age. By using appropriate constraints on survival parameters in Capture-Mark-Recapture (CMR) models, we propose a first analysis of the suitability of the Gompertz and the two-parameter Weibull models for describing aging-related mortality in free-ranging populations of ungulates. We first show how to handle the Gompertz and the two-parameter Weibull models in the context of CMR analyses. Then we perform a comparative analysis of senescence patterns in both sexes of two ungulate species highly contrasted according to the intensity of sexual selection. Our analyses provide support to the Gompertz model for describing senescence patterns in ungulates. Evolutionary implications of our results are discussed
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".