Spatiotemporal patterns of rockfish bycatch in US west coast groundfish fisheries: opportunities for reducing incidental catch of depleted species
Bibliographic record
Abstract
Spatial and temporal management measures to reduce nontarget catch are important strategies for rebuilding overfished rockfish (Sebastes spp.) populations in the Northeast Pacific. We describe efforts to support reducing rockfish bycatch in central California trawl fisheries by testing the efficacy of move-on rules on catch data from 2002 to 2010. Move-on rules are regulations or guidelines that trigger the temporary closure of a fishery in a targeted area when a bycatch threshold is reached, without the closure of the entire fishery. Move-on rules based on spatiotemporal autocorrelation (clustering) were effective in reducing bycatch with modest impact on target catch, removing between 35% and 77% of future hauls with bycatch within a specified distance and time of a bycatch-containing haul, while foregoing target catch by an average of 12%. The spatial and temporal peak clustering scales show correlation with the level of schooling behavior by each species; however, the efficiency of the rules measured either in reduction in bycatch hauls or diminished target catch was not strongly affected by those aggregation behaviors. Our analysis provides information for fishers, such as those in the California Risk Pool, in the continuing development of responses that are more refined in both scale and impact.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".