Hierarchical analysis of a remote, Arctic, artisanal longline fishery
Bibliographic record
Abstract
Abstract Dennard, S. T., MacNeil, M. A., Treble, M. A., Campana, S., and Fisk, A. T. 2010. Hierarchical analysis of a remote, Arctic, artisanal longline fishery. – ICES Journal of Marine Science, 67: 41–51. This is the first paper to explore trends in catch per unit effort (cpue) through time of a Greenland halibut Reinhardtius hippoglossoides stock targeted by an artisanal, winter fishery in Cumberland Sound on southern Baffin Island, Canada. We modelled cpue data from 1987 to 2003, looking at two questions: what factors have driven cpue trends, and is cpue an accurate index of a stock's abundance? In the context of limited data availability, we used generalized linear models (GLMs) and hierarchical models to assess important predictors of cpue. Hierarchical models with multiple fixed environmental effects contained fishing location or individual fisher as random effects. A month effect showed greatest catch rates during February and March; the monthly North Atlantic Oscillation index was positively associated with catch rates; and a change from decreasing to increasing cpue after 1996 was linked to reduced fishery participation following a large storm. The best Akaike's information criterion-ranked GLM identified a negative relationship of cpue with shark bycatch. Although data limitations precluded conventional stock assessment, our models implicated the environment and fisher behaviour as drivers of cpue trends. Additionally, using multiple hierarchical models to predict cpue provided a more informative analysis for understanding trends in cpue than a GLM alone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".