Indirect estimation of contact selectivity for gill nets using hierarchical models
Bibliographic record
Abstract
Indirect estimation of selectivities for gill nets is frequently conducted to evaluate size-related differences in vulnerability, with catches typically pooled across multiple net sets when estimating. An alternative to pooling catches would be to use hierarchical modeling to estimate selectivities, which would allow for variability in selectivity among sets to be assessed and to account for spatial or temporal autocorrelation across sets. We estimated selectivities for gill nets using several hierarchical model formulations using walleye Sander vitreus catch data from two experimental gillnet configurations in Lake Erie. Hierarchical selectivity curves were more supported from an information-theoretic perspective than nonhierarchical versions, although convergence and model complexity issues arose for some models incorporating spatial autocorrelation. Hierarchical bi-normal selectivity curves, where set-specific parameter deviations were modeled through univariate normal distributions were most supported by available data for both configurations. Given the availability of flexible software for fitting and diagnosing hierarchical models, we recommend that hierarchical modeling of contact selectivity be considered to improve understanding of set-specific variability in selectivities with multiple deployments rather than automatically pooling catch data.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".