Estimating the Energetic Contribution of Polar Bear (<i>Ursus maritimus</i>) Summer Diets to the Total Energy Budget
Bibliographic record
Abstract
Polar bears (Ursus maritimus) are faced with potential drastic changes to their ice habitat in the near future. Climate models predict that the ice-covered period during which bears use the sea ice to hunt seals is getting shorter, and that the ice-free season will become extended. Bears will not have enough ice time to accumulate the necessary body fat reserves they need to live off when stranded on land during summers. However, polar bears have been observed making use of several food sources while on land, although the energetic contributions of these diets to the bears’ energy budget were considered to be minor. We examine mathematically whether observed diets (i.e., arctic charr [Salvelinus alpinus], ringed seal [Pusa hispida] blubber, and berry diets) can contribute sufficient energy to offset the daily body mass loss. We then estimate the amount or mass of the diet that must be consumed to achieve a balanced daily energy loss, and whether this is possible, given specific constraints on feeding. The analysis indicated that it is possible for polar bears to maintain their body mass while on shore by feeding on arctic charr and seal blubber. Polar bears of body masses up to 280 kg could gain sufficient energy from blueberries to match the daily energy loss. The question that arises is how many bears of a population would resort to such strategies? To better understand how polar bears will adapt to a. warming climate, we recommend continued examination of polar bear diets in the field, and controlled feeding trials with captive polar bears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".