Comparative assessment of metrics for monitoring the body condition of polar bears in western Hudson Bay
Bibliographic record
Abstract
Abstract Many species experience prolonged periods of fasting due to changes in habitat and food availability. Metrics that quantify energy reserves available during these periods allow for a better understanding of the interaction between environmental change and species survival. Body condition of polar bears has been assessed using morphometric and subjective indices, lipid content of adipose tissue, body composition models and, recently, bioelectrical impedance analysis (BIA). We assessed the utility of BIA and examined correlations among condition metrics for 134 free‐ranging polar bears on shore in western Hudson Bay in fall 2012–2013 and spring 2013–2014. We also examined long‐term inter‐annual and seasonal trends from 736 bears handled in 2004–2014. Total body fat, as estimated from BIA, was correlated with adipose tissue lipid content, energy density and fatness index, but not storage energy or skull width. Body condition was higher in adult and subadult females than males, consistent with energetic demands of gestation and lactation. Adult females had higher body fat in the fall than spring, and body fat decreased with increasing number of dependent offspring. Long‐term trends indicated a decline in body condition for all adult and subadult males and females. Although there were similar patterns among BIA and other established metrics, its limitations in the field suggest that BIA may not be the most efficient method of monitoring body composition in polar bears in comparison to other modeled metrics, such as energy density. Declines in polar bear body condition over time may be a reflection of contemporaneous changes in sea ice availability and population demography, and thus have implications for the long‐term conservation of this subpopulation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".