Is catch proportional to nominal effort? Conceptual, fleet dynamic, and statistical considerations in catch standardization
Bibliographic record
Abstract
Often, catch-per-unit-effort (CPUE) standardizations are used to reflect fish abundance. This implies that catch is directly proportional to effort. We examine this using 78 reported catch and effort series in a meta-analysis, correcting for errors-in-variables in the relationship. Though proportionality in the apparent relationship is the average, there is significant variation among fisheries. We then examine the Scotian Shelf haddock (Melanogrammus aeglefinus) fishery in detail. We used a generalized linear mixed model (GLMM) predicting catch-per-set, accounting for annual and within-year variation in fish, fleet activity, aggregation, and vessel locations and differences. In individual trawls, the catch was less than expected from a proportional relationship with effort. The GLMM revealed both interference and facilitation among vessels as well as autocorrelation among sets. The greatest impact on coefficient estimates was seen by allowing the effort coefficient to vary. Temporal aggregation made the catch–effort relationship appear more proportional. We recommend that fisheries researchers standardize catch explicitly, rather than CPUE, and use disaggregated data to more closely match the underlying relationships in the fisheries being examined.
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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 | 0.306 | 0.509 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".