Description of the monthly spatial dynamics of commercial and bycatch demersal species in the Bay of Biscay
Bibliographic record
Abstract
The global environmental crisis challenges the sustainable management of marine resources, with declining fish stocks threatening both livelihoods and biodiversity. In multi-species fisheries, non-selective fishing gear often results in the capture of non-target species (bycatch), including those subjects to catch limits under the European Union's landing obligation. This regulation introduced the concept of 'choke species': species whose quota is reached but that continue to be caught incidentally, forcing fishing to stop-even if other quotas are still available. In the Bay of Biscay mixed demersal fishery, such technical interactions are linked to combination of targeted species - such as sole (Solea solea), megrim (Lepidorhombus whiffiagonis) and white anglerfish (Lophius piscatorius) - and less targeted or non-target species such as thornback and cuckoo rays (Raja clavata, Leucoraja naevus). To inform these complex interactions, we provide datasets for these five key demersal species including: (1) high-resolution spatial distribution maps of juvenile and adult stages; (2) comprehensive life-history parameter summaries incorporating uncertainty; and (3) structured data designed for integration into spatially explicit models.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".