Performance of time-varying catchability estimators in statistical catch-at-age analysis
Bibliographic record
Abstract
We used Monte Carlo simulations to evaluate how different methods of estimating fishery catchability within statistical catch-at-age analysis (SCA) performed when fishery catchability changed over time. Data-generating models included cases where catchability changed abruptly or gradually over time and where catchability was explicitly a function of population abundance, and we considered corresponding estimation models. In many cases, including fishery effort data in the estimation model and allowing catchability to follow a random walk provided the best (or nearly best) estimates of biomass in the last year as measured by the median of the absolute value of the relative error. Exceptions were cases where fishing mortality was low and catchability trended over time. The estimation model that ignored fishery effort data performed well in cases with a good survey, but performance degraded as survey precision decreased. The white noise estimation model performed poorly in situations where catchability trended over time. No estimation model was best for all underlying models of catchability, but the random walk estimation model performed well under most circumstances and should be used as a starting point for SCAs.
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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 | 0.075 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".