Spatiotemporal modeling of bycatch data: methods and a practical guide through a case study in a Canadian Arctic fishery
Bibliographic record
Abstract
Excess bycatch of marine species during commercial fishing trips is a challenging problem in fishery management worldwide. The aims of this paper are twofold: to introduce methods and provide a practical guide for spatiotemporal modelling of bycatch data, as well as to apply these methods and present a thorough examination of Greenland shark (Somniosus microcephalus) bycatch weight in a Canadian Arctic fishery. We introduce the spatially explicit two-part model and offer a step by step guide for applying the model to any form of bycatch data, from data cleaning, exploratory data analysis, variable and model selection, model checking, to results interpretation. We address various problems encountered in decision making and suggest that researchers proceed cautiously and always keep in mind the aims of the analysis when fitting a spatiotemporal model. Results identified spatiotemporal hotspots and indicated month and gear type were key drivers of high bycatch. The importance of onboard observers in providing robust bycatch data was also evident. These findings will help to inform conservation strategies and management decisions, such as limiting access to spatial hotspots, seasonal closures and gear restrictions.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".