Detecting juvenile survival effects of habitat actions: power analysis applied to endangered Snake River springsummer chinook (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Using 10 years of parr-to-smolt survival rate estimates for passive integrated transponder (PIT) tagged, endangered wild Snake River chinook (Oncorhynchus tshawytscha) (measured in eight spawning streams), we demonstrate that moderate increases in base-case survival may be detectable quickly, as required by recent U.S. National Marine Fisheries Service regulations. The regulations require that effects of tributary habitat actions on juvenile salmonid survival rates be detectable within 5 to 8 years. Simple log-linear regression models were employed where the natural log of survival is a function of parr size, parent stock abundance, tagging location, and year. The analysis uses beforeafter controlimpact (BACI) statistical techniques. The results suggest that multiplicative survival rate changes of 30% should be detectable within 7 years, and increases of 50% within 3 years. Models with higher information-theoretic weights were more powerful than less plausible models. Models using juvenile survival were substantially more powerful than models using spawnerrecruit data for the same stocks, but the analysis is no substitute for field studies, which are still very rare.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".