Escaping the tyranny of the grid: a more realistic way of defining fishing opportunities
Bibliographic record
Abstract
A large part of fishing behavior is choosing where to fish. Trawl skippers usually choose between known fishing opportunities, which are observed as groups of trawls that are conducted in the same portion of a fishing ground, or go exploratory fishing. We outline a simple clustering method based on Euclidean distances between trawls that offers a more realistic way of defining fishing opportunities than grid cells or statistical areas. The resulting cluster tree of trawls is divided into individual groups of trawls (fishing opportunities) using a recommended cut point. Our method correctly classified simulated trawls into fishing opportunities. Fishing opportunities were obtained for vessels in the British Columbia groundfish trawl fishery; each vessel usually fished at a wide variety (mean 26, standard deviation 16, range 269) of fishing opportunities. Within each fishing opportunity, trawls generally caught similar species. In the Argentina scallop fishery, our method was able to divide exploratory from regular fishing trawls, with obvious applications for catch-per-unit-effort calculations. Our method could also be used to detect positional errors in data from these fisheries. Fishing opportunities could provide indications of how fishermen might react to marine protected areas and to the imposition of quotas on multispecies 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".