Technical considerations for stock status and limit reference points under the fish stocks provisions
Bibliographic record
Abstract
Revisions to Canada’s Fisheries Act have resulted in a need for a single limit reference point (LRP) and metric of stock status for major fish stocks prescribed by regulation. The Science Sector has identified a need to provide guidance to estimate LRPs and stock status for scenarios that presently do not meet the “one stock, one LRP, one status” requirement, and a more general need for guidance on methods to estimate and report both LRP and stock status across a spectrum of data and knowledge availability and quality. To inform this guidance, we provide a review of literature and approaches to defining LRPs, describe technical considerations for choosing from various approaches for estimating LRPs and indicators of stock status across the data spectrum, and provide technical considerations and guidance for estimating a single LRP and metric of stock status in cases where either a single assessment model or multiple models are applied. We review methods to estimate BMSY and B0; theoretical, historical, and empirical proxies for these indicators; and some generic “rules of thumb” for other common LRPs used in Canada. We also provide examples of less common indicators, LRPs, and stock status estimation methods that may be applicable across the data spectrum, and review approaches with which to address volatility in stock status indicators. We provide operational and technical considerations as a basis from which to select or reject various candidate indicators and LRPs as well as options and considerations for reporting a single status per stock in an assessment or stock status update, and across advice and management frameworks.
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 | 0.055 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".