How reliable are the abundance indices derived from commercial catch–effort standardization?
Bibliographic record
Abstract
Commercial catch-per-unit-effort (CPUE) data are often standardized to construct indices of stock abundance. The value of such standardization lies in the improvement in the proportionality between the derived index and true abundance. Using the Torres Strait rock lobster ( Panulirus ornatus ) fishery in Australia as an example, we first standardized the commercial CPUE data using a generalized linear model (GLM) and then fitted observation error models to the resulting abundance indices and independent abundance data (as estimated by research diver surveys) to examine the proportionality. While the GLM standardization greatly improved proportionality in comparison with the nonstandardized commercial catch rates, it could produce biased results if the model did not explicitly incorporate variables that had caused changes in fishing efficiency. As most catch–effort standardizations do not model the fishing power component simultaneously, this result may serve as a warning to the potential bias in stock abundance indices extracted from GLMs that are underfitted.
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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 | 0.070 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".