www.fisheries.ubc.ca/publications/working/index.php Comparative Studies on Fisheries Management Strategies in Canada and
Bibliographic record
Abstract
This article sets out the following targets: to describe and analyze fisheries management strategies in Canada and the USA, compare FM in these two economies according to certain important criteria, single out common and specific problems of FM in those two developed economies, draw out conclusions and define what strategies are more applicable to Russian fisheries development. The analysis and comparison of FM in Canada and the USA is made according to the following parameters: management structures, management strategies and regulations, fisheries resource allocation (ITQs and IFQs), fisheries resources status and ecological aspect (EBFM approach). In the introduction, the importance of a new direction in marine fisheries-marine bio resources management is highlighted. This direction has the goal of promoting solution of the most important task of the third millennium- development of sustainable and responsible fisheries. I also consider it important to give a definition of the notion of marine bioresources management in the international law and stress the importance of political component in fisheries governance. 1
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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 | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.262 | 0.057 |
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".