Use of naturally occurring mercury to determine the importance of cutthroat trout to Yellowstone grizzly bears
Bibliographic record
Abstract
Spawning cutthroat trout (Oncorhynchus clarki (Richardson, 1836)) are a potentially important food resource for grizzly bears (Ursus arctos horribilis Ord, 1815) in the Greater Yellowstone Ecosystem. We developed a method to estimate the amount of cutthroat trout ingested by grizzly bears living in the Yellowstone Lake area. The method utilized (i) the relatively high, naturally occurring concentration of mercury in Yellowstone Lake cutthroat trout (508 ± 93 ppb) and its virtual absence in all other bear foods ([Formula: see text]6 ppb), (ii) hair snares to remotely collect hair from bears visiting spawning cutthroat trout streams between 1997 and 2000, (iii) DNA analyses to identify the individual and sex of grizzly bears leaving a hair sample, (iv) feeding trials with captive bears to develop relationships between fish and mercury intake and hair mercury concentrations, and (v) mercury analyses of hair collected from wild bears to estimate the amount of trout consumed by each bear. Male grizzly bears consumed an average of 5 times more trout/kg bear than did female grizzly bears. Estimated cutthroat trout intake per year by the grizzly bear population was only a small fraction of that estimated by previous investigators, and males consumed 92% of all trout ingested by grizzly bears.
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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 | 0.001 |
| 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".