Hierarchical analysis of relationships between brook trout (<i>Salvelinus fontinalis</i>) density and stream habitat features
Bibliographic record
Abstract
We used hierarchical linear regression to examine relationships between brook trout (Salvelinus fontinalis) density and habitat features nested at three levels: sections within reaches, reaches within streams, and streams within a basin. Brook trout density and environmental variables were quantified at 600 stream sections distributed among 120 reaches and 22 streams in the Cascapedia River basin, Quebec, Canada. Decomposition of variance showed that variation in density among streams was small relative to that among sections or reaches and not statistically significant. Density was influenced by habitat variables at both the section (current velocity, woody debris, cover) and reach (subbasin area, height increment at flood, valley width) levels. A cross-level interaction between current velocity and subbasin area pointed to a "contextual" effect: density showed stronger decline with current velocity in larger subbasins than in smaller subbasins. This result suggests that there was no single "best scale" for examining fish–environment relationships. Accounting for contextual effects by use of hierarchical models can enhance our understanding of how habitat features influence fish densities at multiple spatial scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".