Physical constraints on trout (<i>Oncorhynchus</i> spp.) distribution in the Cascade Mountains: a comparison of logged and unlogged streams
Bibliographic record
Abstract
The upstream extent of coastal cutthroat (Oncorhynchus clarki clarki) and rainbow (Oncorhynchus mykiss) trout distribution in logged and unlogged streams of the western Cascade Mountains appears to be primarily constrained by steep channel gradient and sparse pool habitat. Narrow or intermittent wetted channels are also important constraints in logged drainages. The upstream extent of trout distribution appears to be resilient to the combined impacts of historic and current forest management activities, in the absence of impassable road culverts. The probability of trout presence decreased with channel gradient and increased with pool abundance in both logged and unlogged streams, as indicated by logistic regression analysis of physical stream attributes flanking the trout distribution limit in 37 logged and 21 unlogged streams. Reductions in wetted channel width reduced the likelihood of trout presence in logged streams. Logistic regression models fit to data from logged drainages generated accurate predictions of trout presence or absence when applied to data from unlogged drainages. The pervasive extent of native trout in the channel networks of the Cascade Mountains emphasizes the ecological importance of small streams in watershed planning.
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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 | 0.000 | 0.001 |
| 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".