Estimating route-specific passage and survival probabilities at a hydroelectric project from smolt radiotelemetry studies
Bibliographic record
Abstract
A tagrelease study is illustrated using radio-tagged chinook (Oncorhynchus tshawytscha) smolts to concurrently estimate passage rates and survival probabilities through the spillway and turbines of a hydroelectric project. The radio antennas at the forebays of the dam were arranged in double arrays allowing the estimation of route-specific detection probabilities and converting smolt detections to estimates of absolute passage. A maximum likelihood model is presented using the downstream detection histories to jointly estimate the route-specific passage and survival probabilities. In turn, these estimates were combined to estimate smolt survival through the dam, pool, and the entire hydroelectric project. The detailed migration information derived by these techniques can be used to evaluate mitigation programs focused on improving downstream passage of migrating salmonid smolts. At a mid-Columbia River hydroproject, the average spillbay survival calculated across replicate releases of hatchery and run-of-river yearling chinook salmon smolts was 1.000 ( estimated standard error, [Formula: see text] = 0.0144). Average survivals through the two different powerhouses at the hydroproject were estimated to be 0.9409 ([Formula: see text] = 0.0294) and 0.9841 ([Formula: see text] = 0.0119). Project survival after combining the route-specific survival and passage probabilities was estimated across stocks to be 0.9461 ([Formula: see text] = 0.0016).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".