Prevalence and patterns of scavenging by brown bears (<i>Ursus arctos</i>) on salmon (<i>Oncorhynchus</i> spp.) carcasses
Bibliographic record
Abstract
Scavenging, an underappreciated mechanism of prey consumption for many predators, can contribute substantially to nutritional intake. Facultative scavengers such as brown bears (Ursus arctos Linnaeus, 1758) may both kill and scavenge Pacific salmon (genus Oncorhynchus Suckley, 1861), though the extent of scavenging and factors affecting this behavior are unclear. We tagged 899 sockeye salmon (Oncorhynchus nerka (Walbaum in Artedi, 1792)) carcasses and placed them on streambanks over 5 years at multiple sites in southwestern Alaska (USA) where brown bears annually prey on spawning sockeye salmon. Examination of carcasses revealed overall scavenging rates of 15% after 1 day and 54% after 3 days. Scavenging rate varied by site and year and increased throughout the salmon run. Contrary to predictions, scavenging was more frequent in senescent or bear-killed carcasses than ripe carcasses. Carcass consumption ranged from minimal to almost complete; body and brain tissues were most frequently consumed after 3 days (68% and 63% of carcasses, respectively). We also documented secondary scavenging (i.e., tissue consumption on two separate events) and delayed scavenging (i.e., scavenging observed after 3 days but not 1 day). Taken together, the results indicated that scavenging in these streams contributes significantly to total consumption of salmon by bears, with ramifications for other components of these salmon-dependent ecosystems.
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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 | 0.001 |
| 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".