The schooling and foraging ecology of lake herring (<i>Coregonus artedi</i>) in Lake Opeongo, Ontario, Canada
Bibliographic record
Abstract
We used a combination of suspended gill nets and hydroacoustics to investigate the schooling behaviour of lake herring (Coregonus artedi) in Lake Opeongo, Ontario, Canada. Lake herring form schools during the day but are dispersed at night and this change occurs at a light threshold of roughly 0.04 lx. Schools range in maximum linear dimension from 100 to 2300 cm with the majority under 1000 cm. The light threshold for school formation is well below that at which their principal predator, lake trout (Salvelinus namaycush), are able to detect prey. This suggests that schooling may provide advantages in addition to predator avoidance. We observed that lake herring stomachs were fuller during the day than at night, indicating that schooling herring forage more efficiently during the day than individual herring do at night. Furthermore, herring stomach fullness increased with school size, suggesting that schooling enhances foraging opportunities for individual members. We speculate that this is due either to social facilitation of feeding when herring are in the presence of conspecifics, or to corporate vigilance, or "many eyes", which allows individual fish to spend less time being alert to predators and more time feeding.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".