Different proxies, different stories? Imperfect correlations and different determinants of fitness in bighorn sheep
Bibliographic record
Abstract
Abstract Measuring individual fitness empirically is required to assess selective pressures and predicts evolutionary changes in nature. There is, however, little consensus on how fitness should be empirically estimated. As fitness proxies vary in their underlying assumptions, their relative sensitivity to individual, environmental, and demographic factors may also vary. Here, using a long‐term study, we aimed at identifying the determinants of individual fitness in bighorn sheep (Ovis canadensis) using seven fitness proxies. Specifically, we compared four‐lifetime fitness proxies: lifetime breeding success, lifetime reproductive success, individual growth rate, individual contribution to population growth, and three multi‐generational proxies: number of granddaughters, individual descendance in the next generation, and relative genetic contribution to the next generation. We found that all proxies were positively correlated, but the magnitude of the correlations varied substantially. Longevity was the main determinant of most fitness proxies. Individual fitness calculated over more than one generation was also affected by population density and growth rate. Because they are affected by contrasting factors, our study suggests that different fitness proxies should not be used interchangeably as they may convey different information about selective pressures and lead to divergent evolutionary predictions. Uncovering the mechanisms underlying variation in individual fitness and improving our ability to predict evolutionary change might require the use of several, rather than one, the proxy of individual fitness.
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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 | 0.005 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".