A comparison of hierarchical models for relative catch efficiency based on paired-gear data for US Northwest Atlantic fish stocks
Bibliographic record
Abstract
Selectivity and catch comparison studies are important for surveys that use two or more gears to collect relative abundance information. Prevailing model-based analytical methods for studies using a paired-gear design assume a binomial model for the data from each pair of gear sets. Important generalizations include nonparametric smooth size effects and normal random pair and size effects, but current methods for fitting models that account for random smooth size effects are restrictive, and observations within pairs may exhibit extra-binomial variation. I propose a hierarchical model that accounts for random smooth size effects among pairs and extra-binomial variation within pairs with a conditional beta-binomial distribution. I compared relative performance of models with different conditional distribution and random effects assumptions fit to data on 16 species from an experiment carried out in the US Northwest Atlantic Ocean comparing a new and a retiring vessel. For more than half of the species, conditional beta-binomial models performed better than binomial models, and accounting for random variation among pairs in the relative efficiency was important for all species.
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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 | 0.050 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".