Assessing the productivity of the scallop stocks in scallop production areas in the Bay of Fundy and scallop fishing area 29W and the impact of two-year projection advice
Bibliographic record
Abstract
The Bay of Fundy (BoF) Scallop Production Areas (SPAs) 1A, 1B, 3, 4, and 6, and Scallop Fishing Area 29 West (SFA 29W) Subareas A, B, C, and D, comprise the majority of catches from the Inshore Scallop fishery in the Fisheries and Oceans Canada Maritimes Region. Each stock area is managed using total allowable catches (TACs) and have annual analytical assessments which use modified versions of a state-space delay-difference population model that provides one-year biomass projections to inform the setting of the harvest level. However, in 2020, the DFO Science Inshore Scallop surveys were cancelled. In the absence of survey data, two-year model projections were used to inform the scallop fisheries in these areas for the 2020/21 fishing season. The objectives of this document are to evaluate the productivity of the BoF and SFA 29W stocks based on the population assessment models, to derive two-year model projections to inform the final TAC decisions for the 2020/21 season, and to evaluate the impact and uncertainty of the two-year model projections. The scallop stocks within the BoF and SFA 29W demonstrate substantial interannual variability in their productivity such that, relative to the use of one-year projections, use of two-year projections as the basis for management decisions over the long term would result in substantial risk of either loss in potential catch or overharvesting. However, for the BoF stocks and in the context of tactical one-year decision making and in the absence of 2020 survey data, these two-year projections provide context for decision making for the 2021 harvest levels. For SFA 29W, the two-year projections are not sufficiently reliable given challenges associated with projecting low biomasses; therefore, these projections are not recommended to inform the 2021 harvest levels for SFA 29W.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".