Hierarchical Bayesian models of length-specific catchability of research trawl surveys
Bibliographic record
Abstract
To estimate absolute abundance from research trawl surveys, the catchability of the fish to the gear must be known or estimated. Using 47 data sets of length-specific catchability, we conducted a hierarchical Bayesian meta-analysis of length-specific catchability for a number of different species groups. It was found that the Bayesian estimates of catchability were seldom near or above 1 for any species or size. This suggests that any assumption that swept-area abundance estimates are in fact absolute abundance would likely underestimate the true abundance. Catchability was higher for haddock (Melanogrammus aeglefinus) than for Atlantic cod (Gadus morhua) of the same size, suggesting behavioural differences between these two species. We found a seasonal difference in catchability with higher catchability for surveys in summerfall than for those in springwinter. The results of this study can be applied both for the reconstruction of fish community structure for ecosystem models and as auxiliary (or prior) information for single-species stock assessment where catchability is estimated within the stock assessment procedure.
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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 | 0.079 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".