Instream complexity increases habitat quality and growth for cutthroat trout in headwater streams
Bibliographic record
Abstract
The extent and availability of suitable habitat is a fundamental factor limiting the abundance of natural populations. In many stream ecosystems, habitat degradation has reduced habitat quality by removing critical habitat features such as pools. We hypothesized that adding pool habitat to streams would increase habitat quality for salmonid fish and improve population productivity. In this study, we used instream structures to add pool habitat to four headwater streams and estimated changes to habitat quality for cutthroat trout ( Oncorhynchus clarkii) across two seasons using a bioenergetic model. Fish populations were monitored over 5 years to evaluate how treatments influenced fish abundance and growth. We found that the proportion of suitable habitat was higher in treatment sections and in artificially created pool habitats. Abundance of young-of-the-year trout was higher in treatment reaches in comparison to controls and the growth of trout across all size classes sampled was higher in treatment reaches. Our results indicate that increasing pool habitat improves habitat quality resulting in increased densities of cutthroat trout and higher fish growth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".