Mechanics of foraging success and optimal microhabitat selection in Alaskan Arctic grayling (<i>Thymallus arcticus</i>)
Bibliographic record
Abstract
Most fishes residing in temperate streams in the Northern Hemisphere are drift-feeders. Despite this fact, little is known about the mechanisms of drift-feeding itself. We used Alaskan Arctic grayling (Thymallus arcticus), an abundant boreal drift-feeder, to examine the effects of water velocity on several aspects of drift-feeding behavior and test predictions of the Grossman et al. (2002) net energy intake model for microhabitat choice. Water velocity had a negative effect on prey capture, a positive effect on holding velocity, and little effect on reactive distance. We also found that dominance was a better predictor of prey capture success than size rank, although neither of these variables influenced holding velocity or reactive distance. The Grossman et al. (2002) model successfully predicted holding velocities of grayling in one Alaskan stream, but not another. Model failure might have occurred due to higher turbulence, increased predation, or interspecific competition with Dolly Varden (Salvelinus malma). These results help inform the study of habitat selection in drift-feeding fishes as well as management and conservation of Arctic grayling.
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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".