DENSITY-DEPENDENT CATCHABILITY OF SPOT PRAWNS (Pandalusplatyceros ) OBSERVED USING UNDERWATER VIDEO
Bibliographic record
Abstract
Understanding how fishing gear catches target species is an important part of understanding the impacts of commercial fishing. Here, we use underwater video to investigate the catch dynamics of spot prawn (Pandalus platyceros) traps, a fishing gear used in a large commercial fishery in British Columbia, Canada. We report, for the first time, interactions that occur as prawns accumulate in traps during the first eight hours of trap deployment at depths between 75 and 100 m, and test whether catchability of prawns depends on the density of prawns in and around traps. We found that prawn catchability decreased as the number of prawns in the trap increased. This process was driven by a diminishing proportion of approaching prawns that made entry attempts, as well as a reduced likelihood of entry attempts being successful as traps filled with prawns. Very few prawns exited traps during observations, but final catch rates demonstrated that exits must have occurred after the termination of our videos. Recording and examining the full 24 hours of a typical soak would clarify how trap saturation is achieved.
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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 | 0.001 | 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".