Retrospective patterns of differential mortality and common year-effects experienced by spring and summer chinook salmon (<i>Oncorhynchus</i> <i>tshawytscha</i>) of the Columbia River
Bibliographic record
Abstract
We used spawnerrecruit data to estimate the instantaneous differential mortality (µ) experienced by seven Snake River spring and summer chinook (Oncorhynchus tshawytscha) stocks relative to six lower Columbia River stocks. We applied 37 Ricker stockrecruit models to these data, incorporating different assumptions about measurement error, transport survival, intrinsic productivity, methods of estimating µ, and common year-effects that affect the survival of all stocks. Estimates of mean µ for the 12 best models ranged from 0.55 to 1.90 (mean of 1.09), implying that passage from Lower Granite Dam to John Day Dam reduced recruitment of 19701990 Snake River broods by an average of 4285% (mean of 66%). Differential mortality was cyclical and moderately high in the 1970s brood years, low for 19801983, near average in 19841989, and high in 1990. Our empirical estimates of µ showed low bias and were between those produced by two mechanistic passage models. The best empirical models included common year-effects, which shifted from generally positive effects on 19521968 brood years to generally negative effects on 19701990 broods. Year-effects were not significantly correlated with two climate indices or with water travel time (the time that water takes to travel down the Columbia River).
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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".