Behavior-dependent selectivity of yellowtail flounder (Limanda ferruginea) in the mouth of a commercial bottom trawl
Bibliographic record
Abstract
To improve the efficiency\nof a commercial bottom trawl for\ncatching yellowtail flounder (Limanda\nferruginea), we studied the behavior\nof individuals in the middle\nof the trawl mouth. Observations\nwere conducted with a high-definition\ncamera attached at the center\nof the headline of a trawl, during the\nbrightest time of day in June 2010\noff eastern Newfoundland. Behavioral\nresponses were quantified and analyzed\nto evaluate predictions related\nto fish behavior, orientation, and\ncapture. Individuals showed 3 different\ninitial responses independent of\nfish size, gait, and fish density: they\nswam close to (75%), were herded\naway from (19%), or moved vertically\naway from (6%) the seabed. Individuals\nprimarily swam in the direction\nof initial orientation. No fish were\noriented against the trawling direction.\nFish in the center of the trawl\nmouth tended to swim along the bottom\nin the trawling direction. Only\nindividuals that were stimulated to\nleave the bottom were caught. Individuals\nin peripheral locations within\nthe trawl mouth more often swam\ninward and upward. Fish that swam\ninward were twice as likely to be\ncaught. Fish size, gait, and fish density\ndid not influence the probability\nof capture. A trawl that stimulates\nyellowtail flounder to orient inward\nand leave the bottom would increase\nthe efficiency of a trawl.
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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 | 0.000 | 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".