Compound thermal indices for two species of salmonids
Bibliographic record
Abstract
ABSTRACT Water temperature is a determinant variable for the overall health of the river ecosystem and aquatic biota, particularly for cold-water fish. Therefore, the characterization of river temperature is essential for the management of thermal habitats. However, currently, river thermal regime characterization is often achieved by calculating numerous thermal indices that are not often related to cold-water fish physiological requirements and thermal preferences. In this study, we developed a compound thermal index (CTI) based on a methodology used to calculate the water quality index (WQI) in Canada. CTI is composed of specific indicators related to the thermal tolerance thresholds for different life stages for two cold-water species (Atlantic salmon and brook trout), providing a simplified measure of the quality of the thermal habitat for these species. CTI was determined in two salmon/trout rivers in Québec, Canada (Ouelle and Ste-Marguerite). The results showed that (i) CTI allowed the characterization and classification of thermal habitat quality; (ii) the thermal habitat degradation was primarily influenced by climate conditions, particularly during warm and dry years with high temperatures and low precipitations; (iii) the improved thermal habitat quality was associated with air temperature and precipitation values close to seasonal normals; (iv) cold tributaries provided excellent thermal habitats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".