Foraging modes and movements of <i>Oncorhynchus mykiss</i> as flow and invertebrate drift recede in a California stream
Bibliographic record
Abstract
Salmonids frequently adapt their feeding and movement strategies to cope with seasonally fluctuating stream environments. Oncorhynchus mykiss tend to drift-forage in higher velocity habitat than other salmonids, yet their presence in streams with seasonally low velocity and drift suggests behavioral flexibility. We combined 3D videogrammetry with measurements of invertebrate drift and stream hydraulics to investigate the drivers of O. mykiss foraging mode and movement during the seasonal recession in a California stream. From May to July (2016), foraging movement rate increased as prey concentration and velocity declined; however, movement decreased in August as pools became low and still. In May, 80% of O. mykiss were drift-foraging, while by July, over 70% used search or benthic-foraging modes. Velocity and riffle crest depth were significant predictors of foraging mode, while drift concentration was a poor univariate predictor. However, top-ranked additive models included both hydraulic variables and drift concentration. A drift-foraging bioenergetic model was a poor predictor of foraging mode. We suggest that infall and benthic prey, as well as risk aversion, may influence late-summer foraging decisions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".