The stability and resilience of management agreements on climate-sensitive straddling fishery resources: the blue whiting (Micromesistius poutassou) coastal state agreement
Bibliographic record
Abstract
How would the formation, stability, and success of an agreement on cooperative management between neighbouring coastal states for a climate-sensitive fishery resource be affected by changes in the distribution and accessibility of the resource within the exclusive economic zones (EEZs)? In scenario 1, the blue whiting ( Micromesistius poutassou ) is harvestable in the EEZs of Norway, Iceland, the Faroe Islands, and the European Union (EU), as well as in the international waters of the Northeast Atlantic and the Norwegian Sea. The Barents Sea is a fringe area for the species, and there are no fisheries for blue whiting there. Hence, Russia is not regarded as a coastal state with respect to the blue whiting fishery. This severely weakens the stability of the coastal state agreement. In scenario 2, the area of distribution of the harvestable stock expands into the Russian EEZ, giving it status as a coastal state with respect to the resource and, thus, a partner in the management agreement. This secures the coastal state coalition the maximum attainable cooperative value and increases the likelihood of a stable coastal state agreement.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".