Analysis of Large-Scale Groundwater-Driven Cooling Zones in Rivers Using Thermal Infrared Imagery and Radon Measurements
Bibliographic record
Abstract
The role of groundwater (GW) discharge on surface water (SW) quantity, quality and temperature is known to be important. Moreover, the effect of GW contributions to river thermal budgets is critical in natural rivers considering that water temperature plays a vital role in fish survival during extreme heat events. The identification of zones with GW input in rivers can, thus, help river management plans. However, detecting these zones at the watershed scale can be a challenge. This work combines thermal infrared (TIR) imagery of rivers and water sampling for radon measurements for better documentation of GW in rivers. The Sainte-Marguerite and Berard Rivers, both located in Quebec, Canada, are known for their abundance of salmonids. Their water temperature profiles were plotted using TIR imagery, and five cooling zones in the Berard River and two for the Sainte-Marguerite River were identified in which notable GW–SW exchange was the suspected cause. Radon concentrations measured within the cooling zones showed clear GW contribution to SW. TIR imagery is an effective and fast way to identify GW seepage at the watershed scale. Radon can be used as a complementary natural tracer of GW in rivers at finer scales. The combination of both methods was shown to be reliable for the identification of GW in rivers. This can help for a better anticipation of GW effects in management plans to deal with extreme heat waves that are predicted to occur more frequently under future climate change scenarios.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".