A novel Bayesian method for making the most of spatial fishery catch and effort data
Bibliographic record
Abstract
Current fisheries monitoring practices in many regions of the world include precise measures of fishing location.However, spatial information is ignored in most current stock assessments, which assume instead that fishery catch per unit effort (CPUE) observations are independent replicate measurements of average stock density.This aspatial approach misleads estimates of stock status and productivity because: (1) harvesters are not random samplers of stock density, and (2) CPUE observations may be spatially autocorrelated.This paper introduces a hierarchical Bayesian method describing the spatial distribution of fishery CPUE.The spatial method is applied to British Columbia sablefish (Anoplopoma fimbria) and compared to traditional aspatial approaches in a stock assessment context.I show that spatial assessments offer less optimistic estimates of stock status and productivity compared to traditional aspatial assessments, and that the area occupied by the commercially exploitable stock is estimated to have declined by 62 percent from 1990 to 2005.iii I thank my senior supervisor Sean Cox, who gave me just enough room to be creative, but not enough to run amok.Thanks to Randall Peterman
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".