A decision analysis of flow management experiments for Columbia River mountain whitefish (<i>Prosopium williamsoni</i>) management
Bibliographic record
Abstract
High spawning flows from Hugh Keenleyside Dam (HKD) on the Columbia River results in dewatering of eggs in mountain whitefish (Prosopium williamsoni) populations, but the ultimate effect on adult abundance depends on the shape of the egg-to-adult recruitment curve. Our decision analysis assessed the benefits of alternative flow experiments while accounting for uncertainties in this relationship and in flows in the Columbia and Kootenay rivers. The value of experimenting depended on the true recruitment relationship, how we quantified experimental benefits, and experimental design. With current uncertainty, the optimal HKD spawning flow (out of 11 alternative flows) was 1699.2 m3·s1. Spawning flows below 1699.2 m3·s1 did not improve egg survival because lower flows rendered high-quality spawning habitat unavailable and increased scour mortality. Two experimental designs, both with higher precision monitoring, had a high probability of detecting the true recruitment curve at reasonable cost. Information from these experiments suggested an optimal spawning flow of 1699.2 m3·s1 if adult abundance were sensitive to egg mortality or 1982.4 m3·s1 if the population were insensitive.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".