Variation in energetic balance among free-ranging polar bears during the spring mating and foraging season
Bibliographic record
Abstract
Large carnivores are capable of consuming substantial biomasses that can significantly alter their body mass and condition over short periods. Here we examine the intra-seasonal variation of polar bear ( Ursus maritimus Phipps, 1774) body mass, energy intake, and condition in the spring from two subpopulations. We evaluate the biological and temporal factors that may have driven changes in body mass of 31 individuals captured and recaptured over 2–39 days and assess whether these changes influenced their estimated condition. Body mass changed by –61 to 33 kg ([Formula: see text] = –2 kg) with bears exhibiting increases in mass with increasing age and decreases with greater initial mass. On average, estimated intake was 57 MJ/day. Estimated daily mass-specific body mass changes exhibited greater variation relative to previous measures in polar bears or brown bears ( U. arctos Linnaeus, 1758). Yet, across all bears, measures of condition remained similar between captures. The marked variation in mass gains or losses highlights the varying behavioral and physiological limitations that influence foraging success within this apex carnivore during a season when two key life history events converge wherein feeding is often reduced during mating activities despite the importance of the spring hyperphagia period to long-term energy balance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".