Comparison of reference points estimated using a size-based method for two high-latitude crab species in the United States and Canada
Bibliographic record
Abstract
We briefly reviewed the decision rules currently used for managing two major high-latitude crab stocks, snow crab (Chionoecetes opilio) and Dungeness crab (Cancer magister), in the United States and Canada and compared them with model-based reference points, harvest rate, and biomass proportion relative to virgin biomass, developed using species- and area-specific parameters. The model followed a size-based approach, which incorporated BevertonHolt and Ricker stockrecruitment models and estimated mean and median reference points. The recruitment was also perturbed to generate distributions of reference points. The BevertonHolt stockrecruitment model provided a lower harvest rate than that of the Ricker model. Harvest rates were lower for combined sexes spawning biomass than for female-only spawning biomass. Increasing the minimum size at first capture and decreasing the handling mortality resulted in increased harvest rates. Changes in fishery duration and timing of fishery open date did not change the harvest rate appreciably. The harvest rates for the Canadian snow and Dungeness crabs were mostly higher than those estimated for the Bering Sea and Southeast Alaska stocks. Reliable estimates of a number of life history parameters are lacking for both species, and hence, the results of this exercise need to be treated in a precautionary manner.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".