The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish
Bibliographic record
Abstract
Lake trout ( Salvelinus namaycush Walbaum, 1792) forages across macrohabitats (offshore vs. nearshore) that likely pose different challenges to the visual system. We examined how differential foraging between macrohabitats challenges the visual system by sampling lake trout from four lakes with differing food web structures (e.g., presence/absence of cisco/smallmouth bass) and evaluating the association between nearshore energy use and visual traits. We found that lake trout foraging more nearshore had relatively larger eyes (corrected for body size) and larger optic tectum size only in lakes with the large pelagic cisco prey absent, while there was no association with these visual traits when cisco was present. Eye shape was not influenced by nearshore foraging. Our results suggest that nearshore foraging challenges the visual system more than offshore foraging but that this may be dependent on important food web structural attributes.
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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".