Experimental Mixed Gillnets Improve Catches of Narrow-Barred Spanish Mackerel (Scomberomorus commerson)
Bibliographic record
Abstract
A new gillnet made from multiple mesh sizes ranging from 125 to 180 mm of stretched mesh (experimental gillnet) was tested under commercial fishing conditions to compare the fishing performance with that of conventional gillnets with a 125 mm mesh opening (control gillnet). Catch efficiency and size selectivity between the two gillnet types were evaluated throughout one year of fishing in three different locations in the waters of Vietnam. Experimental gillnets caught narrow-barred Spanish mackerel (Scomberomorus commerson), spotted mackerel (Scomberomorus guttatus), and wahoo (Acanthocybium solandri) in comparable amounts to the control gillnets, with the moon phase, month, and depth explaining some of the variation in the catch per unit effort (CPUE). An analysis of the size-dependent catch comparison rates and selectivity parameters showed that the experimental gillnets captured a wider range of narrow-barred Spanish mackerel sizes, but with a substantial proportion of individuals larger than those caught by the control gillnets. This is of higher weight per unit effort, and fishing enterprises therefore could improve their economic benefits by using modified gillnets with multiple mesh sizes. Our findings also support the biological and environmental benefits of the modified gillnet size selection, which might also extend to other species.
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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 | 0.000 |
| 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".