Escaping the net: Assessing midwater gear selectivity for the Joint United States and Canada Integrated Ecosystem and Pacific hake (Merluccius productus) Acoustic-Trawl survey
Bibliographic record
Abstract
Acoustic-trawl surveys use trawl catches to validate the species and size composition of fish aggregations detected acoustically. However, certain sizes of fish may be more likely to escape some trawls, which can bias the size and age distribution of the catch used to estimate biomass. To quantify size-selectivity, we studied 3 midwater trawls used for the United States and Canada joint survey of Pacific hake (Merluccius productus). The survey most recently used an Aleutian Wing Trawl (AWT) with different codend liners until 2023, then switched to a Multi-Function Trawl (MFT) in 2025. To prepare for the switch, we assessed differences in escapement and catch rates using recapture nets, and in paired trawls of both net-types sampling the same aggregations. All nets retained greater than 85% of hake longer than 30-cm (age 2+). In general, the MFT was more efficient than the AWT, with near full retention of all sizes. A substantial fraction of small hake (age 0 to 1) escaped the AWT. A power analysis indicated a low probability of detecting differences in escapement from the AWT with different liners. Gear selectivity information is important to improve the accuracy of fishery survey data and account for changes in sampling gear.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".