Data needs and spatial structure considerations in stock assessments with regional differences in recruitment and exploitation
Bibliographic record
Abstract
This study uses a simulation experiment to demonstrate that bias in estimates of spawning biomass is influenced by the spatial configuration of a stock assessment model, whether survey data are used or not and whether an environmental index is available to inform the spatial distribution of recruitment. Stocks with limited movement of postsettlement fish may be spatially structured due to environmental forces that affect larval dispersal and recruitment distribution or from nonuniform spatial exploitation. Data are frequently aggregated across space in stock assessments, thus disregarding this complex spatial structure and possibly introducing bias into estimates of stock status. An operating model (OM) is created that simulates data that are used in a set of estimation models to assess bias. The following experimental factors are considered: (i) using survey data and environmental indices in the assessment; (ii) using disaggregated data (two regions, as generated by the OM) or aggregated data (one region); and (iii) incorporating different patterns in the OM’s regional exploitation and environmentally driven recruitment distribution.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".