Are subarctic nations’ fisheries benefiting from climate change?
Bibliographic record
Abstract
Assessing performance of fisheries through value of catch arguably offers deeper insight than traditional volume-based measures, which overlook large price differences among species. This study applies a novel cod equivalents (codeqs) metric to evaluate changes in the relative value of northern fisheries over since 1950 and to explore whether these shifts correspond with ocean warming trends observed. The results show that while total catch volumes have declined since the 1970s, the aggregated relative value of catches has remained stable at around five million codeqs per year. At the national level, Greenland, Norway, Russia, United States, and the Faroe Islands have experienced increased catch values, largely due to expanded groundfish and shellfish fisheries, while Denmark, Iceland, and Sweden have seen declines. Changes were insignificant for Canada and Finland. Despite localized gains, particularly in the Barents Sea and around Greenland, the analysis provides little consistent evidence that warming oceans have produced overall economic benefits for northern fisheries. Instead, factors such as improved management, changing species composition, and market dynamics appear more influential than temperature trends in shaping the long-term value of Arctic and subarctic 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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".