Predictive Models for Differentiating Habitat Use of Coastal Cutthroat Trout and Steelhead at the Reach and Landscape Scale
Bibliographic record
Abstract
Abstract Steelhead Oncorhynchus mykiss and Coastal Cutthroat Trout Oncorhynchus clarkii clarkii are closely related species that are difficult to differentiate as juveniles and are commonly sympatric at the watershed scale. If Cutthroat Trout spawning and early rearing occur in small streams, it is often difficult to assess which parts of a stream network are dominated by each species. In this study I used catch data from 649 sites in coastal British Columbia to develop quantitative models of species presence and relative dominance as a function of stream size. An independent data set of 561 streams from the USA was used for the cross validation of models developed with data from British Columbia. The relative dominance of Cutthroat Trout or steelhead was predicted using logistic regression with stream and channel width, stream order, watershed area, unit runoff, ecoregion placement, and long-term mean annual discharge (LT mad) as predictor variables. The LT mad was the best predictor of Cutthroat Trout and steelhead dominance, with a correct classification rate of 98% for the entire species range. Costal Cutthroat Trout dominated in reaches or streams where LT mad was ≤630 L/s, and steelhead dominated in reaches where LT mad was >1,000 L/s. The models have practical application for predicting stream-bearing length and area used primarily by each species at the landscape scale of productive capacity relative to habitat threats. Received June 18, 2012; accepted July 17, 2013
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".