Food availability influences angling vulnerability in muskellunge
Bibliographic record
Abstract
Abstract Reduced food availability increases the capture of several fish species, although the mechanisms responsible for how food resources result in increased capture are undefined. Our objective was to quantify the mechanism by which food availability might influence the angling vulnerability of muskellunge ( Esox masquinongy ). We assessed capture rates in the field under different food levels, quantified the behaviors of muskellunge that were either fed or food deprived, and determined if appetite‐related hormones leptin and ghrelin affected fish behavior. Catch rates of fasted muskellunge were more than twice as high as those of fed muskellunge. However, food deprivation and hormonal treatments did not influence laboratory behavior, which suggested that catch rates of food‐deprived muskellunge increased because fish were more receptive to lures. Furthermore, an ecosystem approach should be used to consider forage as a component of management goals.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".