Application of decision analysis to evaluate recovery actions for threatened Snake River fall chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
We used models and decision analysis to incorporate uncertainties into evaluations of two recovery actions for Snake River fall chinook salmon (Oncorhynchus tshawytscha): (i) maximize transportation and (ii) natural river drawdown of four lower Snake River dams. In the retrospective analysis, we compared alternative stockrecruit models and selected one that was consistent with historical spawnerrecruit data and allowed us to implement alternative hypotheses about hydrosystem, hatchery, harvest, and environmental effects. In the prospective analysis, we used this model and posterior distributions of its parameters in a decision analysis framework to compare projected escapements for the two actions over a range of alternative hypotheses. We found that drawdown was most risk averse, producing larger long-term escapements than maximizing transportation under most hypotheses and model assumptions. Maximizing transportation and drawdown produced similar escapements only if we assumed high or increasing estuary and ocean survival rates of transported fish coupled with either severe reductions in harvest rates or insensitivity of upstream survival rates to dam construction and removal. Although there was relatively little information available for Snake River fall chinook (particularly about estuary and ocean survival rates of transported smolts), decision analysis was a useful technique for organizing data, assessing actions over a range of uncertainties, and identifying research priorities.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".