A Framework for Conditioning Operating Models for the Southwest Nova Scotia/Bay of Fundy Spawning Component of 4VWX Herring
Bibliographic record
Abstract
An approach for conditioning operating models is described and demonstrated for use in a management strategy evaluation for the southwest Nova Scotia / Bay of Fundy spawning component of Atlantic Herring in NAFO area 4VWX. This document (1) provides a detailed account of operating model structure and estimation methods; (2) describes a reference case operating model; (3) investigates sensitivities of conditioning to central uncertainties identified for Atlantic Herring; (4) evaluates the impact of these sensitivities to determine a reference set grid of operating models spanning natural mortality rate, growth, resilience, and historical catch levels; (5) identifies those reference set operating models with the most contrasting implications for MSE projection for use in robustness operating models; (6) identifies outstanding sources of uncertainty and specifies robustness operating models that address these uncertainties; (7) references supporting documentation that allow for reproduction of all results. The reference set of operating models spanned a range of current stock status, magnitude of the current stock and the sustainable rate of exploitation. Additionally, 25 robustness operating models were specified that encompassed an additional eight sources of uncertainty in Herring fishery dynamics and can be used to further discriminate among candidate management procedures.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".