Spatial and temporal distributions in the Norwegian cod fishery
Bibliographic record
Abstract
Abstract Fisheries are characterized by variations in space and time. This study investigates the characteristics of seasonality in cod trawl fisheries in two distinct areas: the coast along the northern Norway and the high sea area of the Barents Sea. Catch per unit effort (CPUE) is used to proxy variation in stock abundance. A CPUE function has been estimated in the frequency‐domain framework, to detect the presence of seasonality. Our analysis reveals that seasonality in stock abundance is only present in the northern coast of Norway. We conclude that as a consequence of seasonality in stock aggregation during the first quarter of the fishing year, possible economic losses caused by reduced prices—stemming from a large supply of cod—are larger than the economic benefits from cost reduction per unit of harvest. We speculate that declined price and consequently potential economic losses encourage trawlers to substitute cod by other high‐value fisheries during the winter months. As the price of cod starts to rise after the first quarter, trawlers begin to target cod in the high sea areas, a region with less seasonality.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".