Feasible stock trajectories: a flexible and efficient sequential estimator for use in fisheries management procedures
Bibliographic record
Abstract
We describe a sequential estimation approach designed to be used as part of a fisheries management procedure; it is computationally efficient and able to be applied to varying types, and extents, of data. The estimator maintains a pool of stock trajectories, each having a unique combination of model parameters (e.g., stock–recruitment steepness) sampled from prior probability distributions. Each year, for each trajectory, the values of variables (e.g., current biomass) are updated and tested against specified constraints. Constraints further determine the feasibility of the trajectories by defining likelihood functions for model variables, or combinations of variables, in particular years. Trajectories that fail to meet one or more of the constraints are discarded from the pool and replaced by new trajectories. Each year, stochastic forward projections of the trajectories in the pool are used to determine an optimal catch level. The flexibility and accuracy of the estimator is evaluated using the fishery for snapper, Pagrus auratus , off northern New Zealand as a case study. The sequential nature of the algorithm suggests alternative methods of presentation for understanding and explaining the fisheries estimation process. We provide recommendations for both the evaluation and operation of management procedures that employ the estimator.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".