A Naive Simulator for a Harvest Control Rule for the West Greenland Fishery for P. borealis
Bibliographic record
Abstract
The Northern shrimp (Pandalus borealis) occurs on the continental shelf off West Greenland in NAFO Divisions 0A and 1A–1F in depths between approximately 150 and 600 m. Greenland fishes this stock in Subarea 1, Canada in Div. 0A. In connection with the certification of the Greenland fishery by the Marine Stewardship Council there has been interest in developing a Harvest Control Rule. A naive simulator for a Harvest Control Rule based on mortality- and biomass-risk criteria, and its application to a stock with simple Schaefer dynamics and a management system based on a surplus-production model, was written for Microsoft Excel. Preliminary conclusions were that, as expected, more conservative mortality-risk criteria can ensure a safer mean level of biomass, with some cost in lower catches. Harvest Control Rules in which the mortality risk could be higher when biomass risk was low and vice versa appeared to be no better—if anything worse—than ones in which mortality risk was kept the same. For the stock-dynamic model simulated, limiting the permissible increase or decrease of catches appeared to bear a cost in lower mean biomass and lower mean catch.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".