Empirical relationships between watershed characteristics and coho salmon (<i>Oncorhynchus kisutch</i>) smolt abundance in 14 western Washington streams
Bibliographic record
Abstract
We assembled data on coho salmon (Oncorhynchus kisutch) from 14 streams in western Washington, including annual smolt counts and annual escapement, either as absolute counts or as an index. We also compiled data on large woody debris (number·km%#150;1 of stream), road densities in the watersheds (km road·km%#150;2), gradient of the streams (%), valley slope adjacent to the stream (%), drainage area in the watershed (km2), and pool, pond, and lake areas (m2·km%#150;1). We explored the relationships between habitat variables and two measures of coho production, the maximum production of smolts in the stream (capacity) and the maximum smolts/spawner (productivity). Using the 11 streams with pool and pond counts, we found that pool and pond densities served as good predictors of smolt density (r2 = 0.85 for pools and 0.68 for ponds, independently). Pools produced 0.39 smolts·m%#150;2 and ponds produced 0.07 smolts·m%#150;2 in the multiple regression fit, accounting for 92% of the residual error. We also found that lower valley slopes, lower road densities, and lower stream gradients were correlated with higher smolt density.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".