Altering gillnet soak duration and timing minimizes bycatch and maintains target catch
Bibliographic record
Abstract
Seabirds are one of the most at-risk avian groups worldwide, and incidental catch in fishing practices is one of the top threats for seabirds globally. Seabirds that forage on fish through surface feeding, pursuit-diving, or plunge-diving are particularly vulnerable to bycatch. Bycatch mitigation solutions are therefore a vital component of global seabird conservation, but owing to the episodic nature of bycatch and its involvement of match-mismatch contingencies, results from existing efforts involving gear additions (e.g., lights, flags, or buoys) are highly varied and, at times, reduce target catch. Altering the time during which gear remains in the water and modifying fishing practices based on the activity patterns of target fish and seabirds is a promising option for bycatch mitigation. We experimentally tested best practices for the soak timing and duration of shallow-set gillnets used in the Atlantic herring (Clupea harengus) bait fishery in Newfoundland and Labrador, Canada. We compared catch, bycatch, and seabird activity among control (ca. 24 h) and short (ca. 12 h) set durations that were left to soak overnight or only during daylight hours. Target catch did not differ between control and short overnight sets but was greatly reduced during short daytime sets. Nearly all bycatch, including all seabird bycatch, occurred during the control sets. Seabirds associated with fishing vessels throughout the day. Since the catch of herring in gillnets occurs at night outside of most coastal seabirds' foraging period, we recommend that fishers continue to haul their nets early every morning to minimize the time where shallow-set nets are filled with prey during daytime hours, thereby limiting seabird bycatch risk.
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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 | 0.000 | 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".