Mono- <i>vs</i> multifilament gillnets: effects on selectivity of narrow-barred Spanish mackerel <i>Scomberomorus commerson</i> in the Persian Gulf
Bibliographic record
Abstract
Abstract Iranian coastal fishers targeting narrow-barred Spanish mackerel (Scomberomorus commerson) recently replaced their historical multifilament gillnets with those made from monofilament, evoking management concerns over potential increases in catch-per-unit-of-effort. During 20 fishing days, we compared catches from replicate surface-set gillnets that were identical in terms of mesh size (140 mm stretched opening), length (180 m), depth (30 m), hanging ratio (0.56) and spatio-temporal deployment, but had different materials: multifilament (1.8-mm diameter twisted twine) vs monofilament (0.8-mm diameter twine). Compared with the multifilament gillnet, there was a trend of greater catches (up to 1.3×) of S. commerson and another retained species, mackerel tuna (Euthnus affinis), along with one discarded species, giant catfish (Netuma thalassina) by the monofilament gillnet. However, statistical significance was restricted to E. affinis catches and a bias towards smaller S. commerson. These differences were attributed to species-specific catching mechanisms within gillnet material, with larger S. commerson retained by their teeth in the multifilament and all E. affinis more securely retained by their deeper bodies in the monofilament. Gillnet materials require regulation to preclude excessive effort on fully exploited stocks of species such as S. commerson.
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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".