Water velocity influences prey detection and capture by drift-feeding juvenile coho salmon (<i>Oncorhynchus kisutch) </i>and steelhead (<i>Oncorhynchus mykiss irideus</i>)
Bibliographic record
Abstract
We examined the effects of water velocity on prey detection and capture by drift-feeding juvenile coho salmon (Oncorhynchus kisutch) and steelhead (sea-run rainbow trout, Oncorhynchus mykiss irideus) in laboratory experiments. We used repeated-measures analysis of variance to test the effects of velocity, species, and the velocity × species interaction on prey capture probability, prey detection distance, and swimming speeds during prey capture. We used 3D video analysis to assess the spatial and temporal characteristics of prey detection and capture. Coho and steelhead showed significant, velocity-dependent decreases in capture probability (~65% to 10%, with an increase of velocity from 0.29 to 0.61 m·s -1 ) and prey detection distance, with no effect of species and no velocity × species interaction. Neither velocity nor species affected prey interception speed; fish intercepted prey at their predicted maximum sustainable swimming speed (V max ) at all velocities. Speed of return to the focal point increased significantly with increasing velocity, with no effect of species. At faster velocities, return speeds were faster than V max , indicating potential increases in energetic cost because of anaerobic swimming. The 3D analysis suggests that the reduction in capture probability was due to both reduced prey detection distance and a uniform decline in detection probability within the prey capture area.
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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 | 0.000 | 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".