The relative importance of local, lateral, and longitudinal variables on the development of habitat quality models for a river
Bibliographic record
Abstract
Fish habitat quality models (FHQM) developed for rivers consist of relationships between indices of habitat quality and environmental conditions prevailing within sites (local variables). Given the hierarchical structure of these ecosystems, modeling habitat quality over complete rivers may require the inclusion of variables in FHQM that represent the processes operating over a more complete range of spatial scales. The objectives of this study were to quantify the relative importance of local, lateral (characteristics of the shores), and longitudinal (attributes along the upstream–downstream axis of the river) variables on a FHQM developed for parr of Atlantic salmon (Salmo salar). Parr densities (an index of habitat quality) and local, lateral, and longitudinal variables were estimated in 32 reaches of 200 m. FHQM were developed using analytical units (AU) of 50, 100, and 200 m (length of AU in the upstream–downstream axis of the river). The structure and the explanatory power of FHQM were affected by the AU size. In the study river, 98% of the explanatory power of FHQM was imputed to the effect of local variables.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".