Case Study Applications of LRP Estimation Methods to Pacific Salmon Stock Management Units
Bibliographic record
Abstract
The revised Fisheries Act requires that Limit Reference Points (LRPs) be identified for all major fish stocks. For Pacific salmon, major fish stocks are represented by stock management units (SMUs). An SMU is composed of one or more salmon conservation units (CUs), which are the assessment units under the Wild Salmon Policy, WSP. We introduce methods to estimate LRPs at the SMU level that integrate statuses derived under the WSP at the CU level. We demonstrate and evaluate the LRPs for three case study SMUs: Interior Fraser Coho (Oncorhynchus kisutch), West Coast Vancouver Island (WCVI) Chinook (O. tshawytscha), and Inside South Coast Chum (O. keta) - excluding Fraser River. Methods are divided into two categories: CU status-based LRPs and aggregate abundance LRPs. CU status-based LRPs are recommended as the default method, and are based on the proportion of CUs above levels associated with increased risk of extinction (above ‘Red’ status) under the WSP. Aggregate abundance methods may be used supplementally to meet specific fisheries management requirements. Aggregate abundance LRPs are subdivided into logistic regression LRPs and projection LRPs. Both types of aggregate abundance LRPs are defined at the SMU-level abundances associated with a desired probability of all component CUs being above Red status, but they differ in that logistic regression LRPs are determined directly from historical data while projection LRPs are determined from projections of CU-level population dynamics. We discuss suitability and requirements for the application of the various LRP estimation methods, drawing from the range of data and information availability among the case studies. In general, the application of aggregate abundance LRPs may be limited to SMUs where the CU-level populations covary, as demonstrated for the Interior Fraser Coho case study, and where covariance has not changed over time or, for projection LRPs, those changes can be parameterized.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".