Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model
Bibliographic record
Abstract
Abstract Allocating quotas among stakeholders requires an agreed‐upon formula. If the stock unit spans multiple management jurisdictions, the formula may require updated biomass estimates of the stock's spatial distribution with respect to those jurisdictions. Data for calculating stock biomass often come from fishery‐independent surveys. While stratified random sampling is a common design, strata boundaries may not always align with state or national jurisdictions, requiring post hoc stratification and imputation to calculate area‐specific biomass. The vector autoregressive spatiotemporal (VAST) model was explored as a tool for calculating fish biomass within subareas of a defined stock unit for three different stocks jointly managed by the United States and Canada on Georges Bank in the Northwest Atlantic Ocean. VAST estimated proportions of stock biomass in each nation's waters were compared with an existing allocation algorithm that utilises a loess smooth through the average design‐based swept area biomass from three fishery‐independent surveys. The ability of VAST to impute biomass when no tows occur in a subarea of a survey stratum was demonstrated, as well as the relatively smoother biomass trend compared with design‐based estimates, which may be desirable if the intent is to avoid large inter‐annual swings in allocated quota.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".