Accounting for Uncertainty in Estimates of Escapement Goals for Fraser River Sockeye Salmon Based on Productivity of Nursery Lakes in British Columbia, Canada
Bibliographic record
Abstract
Abstract For certain populations of sockeye salmon Oncorhynchus nerka, spawner and recruit data are either absent or too limited to estimate escapement goals (target abundance of spawners). In some cases, scientists instead use data on productivity of nursery lakes; however, many such analyses have not accounted for uncertainties. We therefore extended a previously developed lake productivity method for estimating escapement goals (the photosynthetic rate (PR) model) by using a Bayesian statistical approach that takes several sources of uncertainty into account. Utilizing data for Fraser River, British Columbia, sockeye salmon stocks, we compared this Bayesian PR method with stock–recruitment analysis. In six of seven cases, probability distributions of spawner abundance goals from the Bayesian PR method were 27% narrower on average than those from the stock–recruitment method. In four of seven cases, the Bayesian PR method produced higher median estimates of target spawner abundance than did stock–recruitment analysis; the other three pairs of estimates were within 7% of one another. We suggest that the Bayesian PR method is a potential alternative to using stock–recruitment data to estimate escapement goals for sockeye salmon populations.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".