Bibliographic record
Abstract
Abstract Landings statistics can be lower than true catches because many fish are discarded or landed illegally. Since many discards do not survive, treating landings as true catches can lead to biased stock assessments. This paper proposes treating catch as censored by bounding it below by the landings, L, and above by cL (for scalar c > 1). We demonstrate the approach with a simulation study, using a Schaefer surplus production model. Parameters were estimated in a Bayesian framework with BUGS software using two sets of priors. Both the traditional true-catch method and a survey-and-effort method (which was landings free) performed worse on average than the censored approach, as measured by the Bayes risk associated with estimates of maximum sustainable yield (MSY) and of an index of depletion (X). Recursive partitioning (regression trees) was used to associate simulation parameters to best-performing methods, showing that higher commercial fish catchability favoured the censored method at estimating X. In conclusion, censored methods provide a means of dealing with discarding and misreporting that can outperform some traditional alternatives.
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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 | 0.019 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".