Risk management within an RFMO - The case of Greenland halibut and NAFO
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.In 2003 NAFO Fisheries Commission established a fifteen year rebuilding plan for Greenland halibut (Reinhardtius hippoglossoides), a valuable straddling stock off the east coast of Canada, following a period of declining biomass and increasing fishing mortality. The rebuilding plan was, however, ad hoc, developed without consultation with NAFO Scientific Council and has been strongly criticized for being risk-prone and inconsistent with the Precautionary Approach. This criticism has been borne out by subsequent assessments of the stock that have shown that fishing mortality increased under the rebuilding plan to over 2.5 times Fmax and four times F0.1. The development of the rebuilding plan reflects how NAFO has traditionally worked - mainly behind closed doors, making selective use of scientific advice and arriving at decisions on TACs and other regulations though a process that is not always transparent. At the NAFO meeting in 2005 a renewed commitment was made to rebuild the Greenland halibut stock based on scientific principles and the Precautionary Approach. This encouraged NAFO Scientific Council to form a study group to evaluate rebuilding options for the stock using a management strategy evaluation approach, utilizing the open-source FLR environment. This is a transparent approach, necessitating involvement from all stakeholders, which aims to find rebuilding strategies that are robust to risk and uncertainty. We report on progress of the study group, describing how a WIKI was used to encourage participation and how a review meeting in Vigo in February 2008 which included scientists, fisheries managers and industry has been instrumental in guiding the approach
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".