A hierarchical model of the relative efficiency of two trawl survey protocols, with application to flatfish off the coast of Newfoundland
Bibliographic record
Abstract
Abstract We present a hierarchical model for survey comparative fishing (CF) experiments (x) to utilize data from several species (s) and x to provide improved estimates of the relative efficiency of one survey protocol compared to another. This model is applied to four flatfish s and two x conducted by Fisheries and Oceans Canada (DFO) in 1995 and 1996. We used a monotone increasing function for relative efficiency, and included spatial effects to account for this important source of variation that was not considered in previous analyses of these data. We provide detailed analyses of the anticipated impacts of the various changes in the DFO survey protocols to better understand the reliability of the results. We show that there were important differences in relative efficiency among s, x, and spatial regions, which, combined with low sample sizes and low catch rates, contributed to poor precision in the estimates of relative efficiency. We conclude that stock assessment models in the future should have a goal of using unconverted survey indices, but also include information on the relative efficiency of trawl survey protocols as prior distributions. This will more adequately account for this important source of uncertainty.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".