Fishing suitability maps: helping fishermen reduce discards
Bibliographic record
Abstract
Discards poses a serious problem for the productivity and sustainability of European fisheries and thus is an important fisheries and ecological management issue to be solved. In this paper we present a statistical tool, based on the random forest technique, that aims to reduce the negative ecological impact of fishing by providing fishermen with near-real-time maps of a fishing suitability index based on haul-by-haul catch and discard rates, indicating the most suitable areas for fishing. These easy-to-interpret maps are to be accessible to users via an online geoportal. Observer data from the Spanish discard sampling program from 2004 to 2008 for several species caught in the Cantabrian Sea (ICES area VIIIc) were used to illustrate the random forest approach. Results in the case study varied among species and seasons, with better results achieved for balanced datasets, such as those for economically valuable target species with segregated life stages. We discuss how this online tool could be useful for fisheries management, particularly in the context of the European Common Fisheries Policy reform and the discard ban on commercial species.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".