Examining feeding strategies and position choice of drift-feeding salmonids using an individual-based, mechanistic foraging model
Bibliographic record
Abstract
We demonstrated the ability of a mechanistic habitat selection model to predict habitat selection of brown trout (Salmo trutta) and mountain whitefish (Prosopium williamsoni) during summer and winter conditions in the Blacksmith Fork River, Utah. By subtracting energy costs and losses from the gross energy intake rate (GEI) obtained through simulation of prey capture, the model calculates the potential net energy intake rate (NEI) of a given stream position, which is essentially the rate of energy intake available for growth and reproduction. The prey capture model incorporates the size, swimming speed, and reaction distance of the fish; the velocity, depth, temperature, and turbidity of the water; and the density and size composition of the drifting invertebrates. The results suggest that during both summer and winter, the brown trout and mountain whitefish in our study reach avoided locations providing low NEI and preferred locations providing a high ratio of NEI to the swimming cost (SC) at the focal position of the fish (NEI/SC). This supports the idea that the drift-feeding fish in this study selected stream positions that provided adequate NEI for the least amount of swimming effort.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".