A voluntary reduction in the commercial catch of rock lobster <i>(Jasus edwardsii)</i> in a New Zealand fishery
Bibliographic record
Abstract
Abstract We describe the development and application of a management procedure (decision rule) that resulted in a voluntary reduction in the commercial catch of spiny rock lobster ( Jasus edwardsii ) in the lower east coast of North Island of New Zealand. The management procedure was developed from an accepted assessment of the CRA 4 (Wellington‐Hawke's Bay) fishery, which used an integrated length‐based assessment model fitted to commercial fishery catch‐per‐unit‐effort (CPUE) biomass indices, commercial length‐frequency data, and tag‐recapture data. The assessment model had been Bayesian, and used the joint posterior distribution of parameters to predict the effect of 384 alternative harvest control rules on the future size of the CRA 4 stock. The harvest control rules all used CPUE as their input, and generated annual changes in catch, which were then simulated by the population dynamics of the operating model. Uncertainty was added to evaluations through observation error, added to the simulated CPUE observations, and stochastic serial auto‐correlation variation in recruitment. We describe how this management procedure was used to effect a voluntary reduction in catch to address the problem of a rapidly declining population.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".