Transportation of Pacific salmon carcasses from streams to riparian forests by bears
Bibliographic record
Abstract
Predation on Pacific salmon by bears (genus Ursus L., 1758) can be an important ecosystem process because the spatial distribution of carcasses largely determines whether marine-derived nutrients cycle through aquatic or terrestrial pathways. Direct observations on three streams in southeastern Alaska indicated that 49% of the pink ( Oncorhynchus gorbuscha (Walbaum, 1792)) and chum ( Oncorhynchus keta (Walbaum in Artedi, 1792)) salmon killed by bears were carried into the forest. The tendency of bears to transport carcasses was independent of the sex and species of salmon, but unspawned fish were more often transported than fish that had completed spawning. Data on tagged sockeye salmon ( Oncorhynchus nerka (Walbaum in Artedi, 1792)) in one southwestern Alaska stream indicated that 42.6% of the killed salmon were transported, and that higher percentages were transported in years when salmon densities were greater. At six other streams, on average, 68.1% of the sockeye salmon killed were apparently transported away from the stream into the forest. Combining the data from all sites, the proportion of carcasses transported increased with water depth at the site. These results emphasize the role that bears play in mediating the interactions between nutrients from salmon and the terrestrial and aquatic ecosystems, and the variation in carcass distribution among streams and among years.
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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 | 0.001 | 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".