Estimation of the area of potential thermal refuges using generalized additive models and multivariate adaptive regression splines: A case study from the Ste‐Marguerite River
Bibliographic record
Abstract
Abstract Thermal refuges in rivers are becoming a critical habitat for ectotherm fish, including Atlantic salmon (Salmo salar). In this study, two statistical modelling approaches were used to estimate the areas of potential thermal refuges: generalized additive models (GAM) and multivariate adaptive regression splines (MARS). This allowed for the first development of a reliable statistical model that uses a few relevant predictors (air temperature, river discharge, main river, and tributary temperatures) to estimate tributary plume thermal refuge surface areas. GAM and MARS models were fitted independently for four sites on the Ste‐Marguerite River, (Quebec, Canada). Model performances were evaluated using the leave‐one‐out cross validation (LOOCV) approach and the following criteria: the Akaike information criterion (AIC), root‐mean‐square error (RMSE), relative root‐mean‐square error (rRMSE), Nash‐Sutcliffe efficiency coefficient (NASH), and finally the bias (BIAS). Using an array of thermographs deployed at the confluence of a cold tributary and the warmer main river stem, refuges were delineated at a daily time step. Model results indicate that the estimated areas are similar to the refuge surfaces interpolated using temperature measurements, with both models and for all sites. Results suggest that MARS performs better than GAM in terms of forecasting and estimating the variability of the area of thermal refuges at all study‐stations. This relatively simple approach will be of use to water resources managers faced with the challenge of protecting thermal refuges for fish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".