Understanding limits to cod and haddock separation using size selectivity in a multispecies trawl fishery: an application of FISHSELECT
Bibliographic record
Abstract
This investigation demonstrates how a multidisciplinary approach based on the FISHSELECT framework, sea trial data, underwater recordings, and laboratory investigations of netting can be applied to size selectivity studies and related management issues. We studied the morphological characteristics of Atlantic cod ( Gadus morhua ) and haddock ( Melanogrammus aeglefinus ) in the Barents Sea bottom trawl fishery. The differences between the L50 values (i.e., the size at which a fish has a 50% chance of being retained) that were recorded for the two species during sea trials can to a large extent be explained by the morphological differences between them. Because of these morphological differences, L50 is always larger for cod than for haddock with the grid and codend setup commonly used in the area. While catch separation of cod and haddock is a management objective in the Barents Sea, we demonstrate that the morphological differences between the species and the grid and codend setup in force today limit achievable differences in L50 to 5.5 cm. Furthermore, we show that for this fishery, the scope for increasing L50 differences between these species by changing the mesh shape configuration of the codend is minimal.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".