Testing a model of drift-feeding using three-dimensional videography of wild brown trout, <i>Salmo trutta</i>, in a New Zealand river
Bibliographic record
Abstract
We tested the assumptions and predictions of a foraging model for drift-feeding fish. We used three-dimensional videography to describe the foraging behavior of brown trout, Salmo trutta, mapped water depth and velocity in their foraging area, sampled invertebrate drift to determine length class specific drift densities, and captured trout to determine the size composition of their diet. The model overestimated the fish's prey capture rate and gross energy intake rate by a factor of two. Most of this error resulted from the fact that prey detection probabilities within the fish's foraging area averaged only half the expected value. This was the result of a rapid decrease in capture probability with increasing lateral distance from the fish's focal point. Some of the model's assumptions were accurate: equations for predicting reaction distance and minimum prey size supported reliable predictions of the shape and size of the fish's foraging area and the size composition of the diet. Other assumptions were incorrect: fish detected prey within the predicted reaction volume, not on its upstream surface as expected, fish intercepted prey more slowly than the expected maximum sustainable swimming speed, and fish captured about two-thirds of their prey downstream of their focal point, rather than upstream.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".