Temporal change in the morphometry–body mass relationship of polar bears
Bibliographic record
Abstract
Abstract Accurate information on animal body mass is often an essential component of wildlife research and management. However, for many large‐bodied species, obtaining direct scale weights from individuals may be difficult. In these cases, morphometric equations (e.g., based on girth or length) may provide accurate and precise estimates of body mass. We developed predictive equations to estimate the body mass of free‐ranging polar bears (Ursus maritimus) in western Hudson Bay, Canada. Using multiple linear and non‐linear regression, we identified a strong relationship between polar bear body weight and linear measures of straight line length and axillary girth. The mass–morphometry relationship appeared to change over time and we developed separate equations for polar bears measured during 2 time periods, 1980–1996 and 2007–2009. Non‐linear models were more accurate and provided body mass estimates within 5.8% (R2 = 0.98) and 6.1% (R2 = 0.98) of scale weight in the earlier and later time periods, respectively. Earlier equations developed for polar bears in this subpopulation performed poorly when applied to recently sampled individuals. In contrast, some contemporary equations from other regions performed reasonably well, suggesting that temporal changes within a subpopulation may be more pronounced than regional differences and can render earlier predictive equations obsolete. Our results have important implications for current and future studies of polar bear body condition and the effects of ongoing climate warming. © 2011 The Wildlife Society.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".