Southwest Nova Scotia/Bay of Fundy Herring: Alternative Candidate Management Procedures: Interim results focused on informing immediate suggestions for the 2023 TAC (revised)
Bibliographic record
Abstract
The performances of a number of Candidate Management Procedures (CMPs) for the Southwest Nova Scotia/Bay of Fundy Herring fishery are investigated, with a focus on contributing towards the information to be considered when recommending a TAC for 2023 for this fishery. Calculations follow recent approaches, being based on the Operating Model for the fishery which manifests the lowest productivity, and meet the current conservation objective criterion. The CMPs considered contrast the trade-offs between avoiding an immediate TAC decrease (if any) that is particularly large, and ensuring both resource and TAC increases in the longer term, as well as future TAC variability that is not too large in the interests of reasonable stability in the industry. Of these CMPs, that which would see the TAC for 2023 decreased by 5% from that in 2022 is considered to provide the best balance between these conflicting objectives. Reasons are given why the calculation approach used is considered unduly conservative, a matter that merits further consideration in the necessary process of developing the herring MSE further before finally adopting a Management Procedure for this fishery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".