Identifying reliable indicators of fitness in polar bears
Bibliographic record
Abstract
Animal structural body size and condition are often measured to evaluate individual health, identify responses to environmental change and food availability, and relate food availability to effects on reproduction and survival. A variety of condition metrics have been developed but relationships between these metrics and vital rates are rarely validated. Identifying an optimal approach to estimate the body condition of polar bears is needed to improve monitoring of their response to decline in sea ice habitat. Therefore, we examined relationships between several commonly used condition indices (CI), body mass, and size with female reproductive success and cub survival among polar bears (Ursus maritimus) measured in two subpopulations over three decades. To improve measurement and application of morphometrics and CIs, we also examined whether CIs are independent of age and structural size-an important assumption for monitoring temporal trends-and factors affecting measurement precision and accuracy. Maternal CIs and mass measured the fall prior to denning were related to cub production. Similarly, maternal CIs, mass, and length were related to the mass of cubs or yearlings that accompanied her. However, maternal body mass, but not CIs, measured in the spring was related to cub production and only maternal mass and length were related to the probability of cub survival. These results suggest that CIs may not be better indicators of fitness than body mass in part because CIs remove variation associated with body size that is important in affecting fitness. Further, CIs exhibited variable relationships with age for growing bears and were lower for longer bears despite body length being related to cub survival and female reproductive success. These results are consistent with findings from other species indicating that body mass is a useful metric to link environmental conditions and population dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".