Size selectivity and length-dependent escape behaviour of haddock in a sorting device combining a grid and a square mesh panel
Bibliographic record
Abstract
Size selectivity of a new sorting section combining a sorting grid and a square mesh panel was tested for haddock (Melanogrammus aeglefinus) in the Barents Sea demersal trawl fishery. Sampling data for a wide size range enabled investigating the selection process for this species in detail, both for the grid and the square mesh panel. Contrary to earlier studies modelling size selectivity for grids and square mesh panels, which assume that the escape behaviour of all sizes of fish is equal, we applied a model that accounted for haddock of different sizes showing different escape behaviours. Our results demonstrated that this model could describe the experimental data collected better than existing models. Specifically, our results showed that the likelihood for smaller haddock to seek escape through the grid and the square mesh panel was higher than that for bigger haddock that still would manage to escape through the devices if they attempted. The new modelling approach presented in this study may be applicable to other species, selection devices, and fisheries.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".