A catch per unit effort - soak time model for the Bristol Bay red king crab fishery, 1991-1997
Bibliographic record
Abstract
Postseason commercial fisheries data are used to estimate a catch per unit effort (CPUE) soak time relationship for the 19911993 and 19961997 Bristol Bay red king crab (Paralithodes camtschaticus) fishery in order to gain regulatory and in-season management insight. Use of commercial fishery data allows our model to capture the influence of biological and environmental effects as well as behavioral responses of crabbers to changing natural and regulatory conditions on CPUE. However, data deficiencies present a variety of estimation challenges, especially when the data are derived from neither a contemporaneous nor a scientifically designed sample of the fleet. A statistical framework for dealing with such challenges is illustrated in this paper. This research uncovered three major results. First, data pooling guided by recursive estimation/hypothesis testing is shown to be essential. Second, the analysis provides insight into CPUE response to changing conditions, whether biological, ecological, or policy induced. Third, it is apparent that more complete and contemporaneous collection of commercial fisheries data is critical to refine the estimation of CPUE - soak time relationships. Then, it may be possible to isolate the inter- and intra-seasonal influence of biological and environmental effects as well as behavioral responses of crabbers to changing natural and regulatory conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".