Collaboratively assessing the impact of a restrictor rope on the catch derived from a bottom trawl survey
Bibliographic record
Abstract
Fishery-independent surveys are vital for marine resource management, offering unbiased assessments of species abundance. Consistent trawl gear performance, however, is challenging across diverse geographic ranges or with multiple vessels. A trawl modification that has repeatedly been shown to improve consistency across a large depth range is the use of a restrictor rope to limit the width of trawl gear. While its stabilizing impact on gear performance is established, its effect on catch rates is less understood. We assessed the utility of this gear component in a collaborative experiment conducted in southern New England, USA, focusing on the impact of a restrictor rope on catches of the region’s commercially important species in a common trawl survey net. Drawing input from regional stakeholders, this experiment partnered with a commercial fishing vessel to conduct alternate tows, with and without a restrictor rope attached to the trawl doors. Results suggest few or subtle impacts of the restrictor rope treatment on total catches or catch-at-length for the examined species. This research offers valuable insights for standardizing gear performance using restrictor ropes.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".