Adjustment of a Bayesian surplus production model for Northern shrimp for stocks in the Gulf of St. Lawrence
Bibliographic record
Abstract
As part of the review of the precautionary approach for northern shrimp in the Estuary and northern Gulf of St. Lawrence (ENGSL), a Bayesian, state-space, surplus production model was fit to commercial landings and the biomass index for each of the four stocks. This document describes the methods used to fit the Bayesian surplus production model to biomass data for the northern shrimp stocks in the ENGSL. According to the results obtained, this model could be useful for assessing stock status and for revising the precautionary approach. The results presented here are consistent with the findings of the previous assessment and show that the Esquiman, Anticosti and Sept-Iles stocks have been declining for several years, with biomass values in 2022 falling to the lowest levels observed since 1990. The results also show that the Estuary stock is currently stable with high biomass values.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".