Approaches to the assessment and management of multispecies skate and ray fisheries using the Falkland Islands fishery as an example
Bibliographic record
Abstract
Eleven rajid species are taken around the Falkland Islands, with four species, Bathyraja griseocauda, Bathyraja albomaculata, Bathyraja brachyurops, and Raja flavirostris dominating commercial catches and generally occurring together. Catch limits for individual species are not used in management because species are not separated in the catch or reported separately. The catch per unit effort for the mixed rajid assemblage was standardised using generalised linear modelling techniques, and two production models were used to estimate stock size and sustainable yield. Maximum likelihood methods were used to demonstrate that there are two distinct rajid communities, one to the north and one to the south of the Falkland Islands, which have different sustainable yields. Changes in species composition over the 10-year course of the fishery confirm theoretical expectations that the larger, later-maturing B. griseocauda is being replaced in catches by the smaller, earlier-maturing B. albomaculata and B. brachyurops. These changes in composition were evident after only 6 years of directed fishing. The current fishery to the north of the Falkland Islands appears to be stable at an annual catch of about 3000 t, which is between 6.5 and 7.6% of the estimated pre-exploitation biomass.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".