Body mass and mast abundance influence foraging ecology of the American black bear (<i>Ursus americanus</i>) in Maine
Bibliographic record
Abstract
We studied nutritional ecology of American black bears (Ursus americanus Pallas, 1780) in Maine, including active and hibernating bears during 5 years, across three study areas, using nitrogen stable isotope analyses of blood samples (n = 152). Our central finding, in two study areas, is positive correlation between body mass and δ15N. This suggests use of large body size to acquire or guard food resources that have relatively high δ15N, consistent with importance of ungulates as food for the largest bears in Maine. In these two study areas, hibernating bears across the spectrum of body mass showed greater δ15N during 2 years of beechnut (Fagus grandifolia Ehrh.) scarcity compared with 2 years of beechnut abundance. Adiposity, measured by serum leptin, was greater in hibernating bears following a season of beechnut abundance compared with one of beechnut scarcity. Total litter mass correlated positively with maternal serum leptin and negatively with maternal δ15N, supporting the importance of mast, including beechnuts, to reproductive success of bears in Maine. In the third study area, bears across the spectrum of body mass had greater δ15N in all years, consistent with food resources relatively high in15N that were available to bears of all sizes.
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 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".