Incorporating parameter uncertainty into evaluation of spawning habitat limitations on Chinook salmon (<i>Oncorhynchus tshawytscha</i>) populations
Bibliographic record
Abstract
Incorporating parameter uncertainty into a Monte Carlo procedure for estimating spawning habitat capacity helped determine that spawning habitat availability is unlikely to limit recovery of six populations of Chinook salmon (Oncorhynchus tshawytscha) in Puget Sound. Spawner capacity estimates spanned up to four orders of magnitude, yet there was virtually no overlap of distributions of capacity estimates with distributions of current spawner abundance (<0.2% overlap), except for the Suiattle River population (51% overlap). Empirical distributions of input parameters contained several important sources of uncertainty, insuring reasonably wide distributions of capacity estimates. The most defensible ranges of input parameters tended to produce conservative capacity estimates, indicating that increased model accuracy would only strengthen our conclusion that spawning habitat is not a constraint on these populations. There are insufficient data with which to develop parameter distributions that better represent historical capacity, which would certainly be higher than our estimates. Our results suggest that factors other than spawning capacity limit population size and that recovery efforts for Skagit River Chinook salmon need not focus on spawning habitat restoration.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".