Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach
Bibliographic record
Abstract
Study region Pristine Atlantic salmon rivers located across northeastern Canada and the U.S. Study focus To simulate water temperature in ungauged rivers, we explore the regionalization of thermal parameters within the CEQUEAU model—a deterministic, semi-distributed hydrological and water temperature model. Additionally, a global sensitivity analysis is conducted to identify the most sensitive thermal parameters within the study region. We employed the support vector regression algorithm (SVR), to map the dependence of these parameters with climatic and watershed characteristics. New hydrological insights for the region Parameters controlling radiative and sensible heat fluxes are the most critical for CEQUEAU water temperature modeling within the study region. Key explanatory variables include low cloud coverage, high wind speed quantiles , upstream land cover areal coverage, distance to the coast, watershed orientation, and topographical features describing surface curvature and elevation. The machine learning-based regionalization approach provides a robust approach for deriving water temperature model parameters from watershed attributes, provided flow measurements are available. Using leave-one-out cross-validation, support vector regression (SVR) significantly outperformed the traditionally used multiple linear regression (MLR), achieving a mean regional RMSE of 1.89 °C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".