Application of decision analysis to evaluate recovery actions for threatened Snake River spring and summer chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
There is uncertainty about the importance of various factors in explaining declines of chinook salmon (Oncorhynchus tshawytscha) populations in the Snake River basin of Oregon and Idaho. This uncertainty has prevented implementation of long-term recovery actions for these stocks. We used simulation models and decision analysis to evaluate three management actions for seven index stocks of Snake River spring and summer chinook salmon: (i) continue current operation of the Columbia River hydropower system, (ii) maximize transportation of smolts, and (iii) natural river drawdown (breaching) of four Snake River dams. Decision analysis provided a useful approach for including multiple hypotheses about population responses to environmental and anthropogenic factors, systematically assessing the importance of alternative hypotheses, and identifying risk-averse recovery strategies that meet survival and recovery goals over a wide range of uncertainties. We found that the most influential uncertainties were related to hypothesized causes of estuary and ocean mortality. Current monitoring provides limited information on survival in this life stage; carefully designed management experiments are more likely to generate useful information. Given that these uncertainties exist, drawdown was the most risk-averse action, meeting long-term survival and recovery goals over a wider range of assumptions than the other actions.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 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".