Juvenile redfish (Sebastes spp.) behavior in response to Nordmøre grid systems in the offshore northern shrimp (Pandalus borealis) fishery of Eastern Canada
Bibliographic record
Abstract
A recent rebound of juvenile redfish (Sebastes spp.) in areas where the northern shrimp (Pandalus borealis) bottom trawl fishery in eastern Canada occurs has been challenging the fishing industry to maintain bycatch of this species within acceptable levels. Using self-contained underwater cameras and red lights, this study investigated the behavior of juvenile redfish in response to bycatch reduction devices (BRDs), called Nordmøre grids. Fish behavior was analyzed in grid systems with different bar spacings, including 22- and 19-mm bar spacings. A total of 10.3 h of useable underwater video was collected during commercial fishing conditions, which yielded individual observations of 931 redfish. Generalized linear models (GLMs) and behavioral trees were used to analyze the data. We observed that 52.5% of all redfish passed through the bar spacings and were retained. The duration of the selection process was relatively short (~1.9 s mean), and 57.8% of redfish reacted to the grids by swimming upwards, forward, or towards with respect to the grids. Behaviors exhibited by redfish and redfish retention were similar for both grids. GLM results suggested that as time in front of the grid increased and redfish had upwards or steady grid reactions, retention was drastically reduced. These were important variables that significantly explained the capture fate of redfish. The behavioral sequence that led to higher escape probability was redfish that approached upwards, had no contact with the grid, and reacted to the grid by continuing to move upwards to finally exit through the escape opening. GLM and behavioral trees gave a comprehensive view of redfish behavior, which is extremely useful for perfecting or developing any BRD to address juvenile redfish bycatch.
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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".