Do single point condition measurements predict fitness in female pronghorn (<i>Antilocapra americana</i>)?
Bibliographic record
Abstract
Condition is frequently used in evolutionary studies as an estimator of fitness. Broad theoretical interpretations of condition include many different attributes that can influence fitness, but in practice, researchers commonly employ condition measures (e.g., body-fat scores or size-adjusted body mass) that have uncertain relationships with reproductive success. In addition, researchers typically rely on condition estimates that are made once. Empirical studies investigating the relationship between condition and fitness are nearly absent. We examined the effect of maternal condition on current and future reproductive success in a wild population of pronghorn ( Antilocapra americana (Ord, 1852)). We used body condition scoring and date of annual molt to measure female condition, and mass–size residuals to measure offspring condition at birth. We found that current reproductive success lowered female condition, and that poor condition reduced subsequent prenatal growth rates. However, poor condition did not reduce postnatal offspring condition or future reproductive success. We suggest that the elapsed time should be taken into consideration when making predictions based on single point condition measures. The assumption that condition predicts fitness requires further empirical test.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".