Catchability and the spatial distribution of fishing vessels
Bibliographic record
Abstract
A central problem when using commercial catch per unit effort (CPUE) as an index of fish stock abundance is that fishing vessels search for concentrations of fish. For a given stock abundance, CPUE may become high if the vessels succeed in finding patches of fish and low if the vessels distribute their catching operations more randomly. In this work, the relationship between catchability and two measures of the degree of spatial concentration of a trawl fleet (the fleet's spatial extent and the fleet's degree of spatial patchiness) is investigated for four different fish stocks. The catchability of northeast Arctic cod (Gadus morhua) is strongly related to the fleet's degree of spatial concentration, but the relationship is weaker for northeast Arctic haddock (Melanogrammus aeglefinus), and no relationships appear for two saithe (Pollachius virens) stocks. Our findings suggest that adjusting CPUE with a measure of the fleet's average degree of concentration relates CPUE more strongly with abundance for migratory stocks.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".