Using vertical distribution to separate fish from crustaceans in a mixed species trawl fishery
Bibliographic record
Abstract
A major challenge in mixed fisheries is achieving acceptable size selectivity for morphologically different species using the same fishing gear. Separator trawls can have different selective properties in the upper and lower compartments and provide successful separation of species. We used a horizontally divided codend with small square meshes (40 mm) and a simple frame to stimulate fish to swim into the upper compartment. The majority of the fish were separated successfully from Nephrops (Nephrops norvegicus), but their preference were uniform. Less than 10% of the Nephrops entered the upper compartment. Length-based analysis revealed three patterns of separation efficiency among nine commercial species: length-dependent separation and preference for the upper or lower compartments. The separation efficiency should be improved for small roundfish and flatfish. There was little diel effect on the separation efficiency. The preference of fish for a compartment, taking the relative height of that compartment into account, was established for this and similar previous studies to enable comparison of results. We recommend length-based analysis to account for the fished population when interpreting results.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".