Statistical tools for thermal regime characterization at segment river scale: Case study of the Ste‐Marguerite River
Bibliographic record
Abstract
Abstract Suitable thermal fish habitats are constrained by both maximum and minimum temperature tolerances. A multivariate and geostatistical approach was developed to estimate stream thermal characteristics at the river segment scale. Data from 22 temperature‐monitoring stations during summer 2007 were used to estimate monthly maximum temperature as well as thermal characteristics such as the number of events, the cumulative degree–days and the associated duration over specific temperature thresholds of 19 and 21°C. The probability of exceeding these temperature thresholds has also been interpolated. The methodology relies on the construction of a multivariate space using physiographic and hydrological characteristics of gauging stations as inputs in a canonical correlation analysis (CCA). A geostatistical interpolation technique, ordinary kriging, was subsequently used to perform interpolation in the physiographical space constructed using CCA. Results from this study were obtained for thermal characteristics estimated into two different interpolation spaces: (1) a 7 metrics space, and (2) an 8 metrics space. Cross‐validation technique has been performed and satisfactory results were obtained. Kriging thermal characteristics (magnitude and duration) into the 7 metric space for a 19°C threshold exceedance leads to best results with Relative Root Mean Square Error (RRMSE) ranging between 9.66 and 15.08%. The study shows that kriging in a multivariate space is a promising tool for water resources managers, especially in cases where risk mapping for lethal or sub‐lethal temperature thresholds may be required for a specific fish species. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".