Impact of a standing column well on the geochemical and microbiological quality of groundwater
Bibliographic record
Abstract
Transitioning to renewable energy sources is crucial in the fight against climate change and ensuring sustainable energy security. Geothermal systems emerge as a promising solution, specifically through the utilization of standing column wells (SCW). These wells, integrated into geothermal systems, offer a greater heat exchange efficiency. However, the implementation of SCWs for heat extraction and injection can impact groundwater geochemistry and bacterial communities. This article explores the repercussions of employing a SCW geothermal system on groundwater for a school in Mirabel, Quebec, Canada. Groundwater sampling was conducted from July 2022 to May 2023 to analyze the geochemical and microbiological aspects of the water quality affected by SCWs used for heating and cooling. This comprehensive study, spanning over a year, revealed some changes in the chemical composition of groundwater, and the absence of several bacterial groups responsible for various bio-corrosion issues, such as iron and sulfate-reducing bacteria. The data also unequivocally demonstrate that the system's operation limits the proliferation of pathogenic groups rather than fostering their multiplication. While the initial findings are promising, this work demands longer-term monitoring. Continued observation over an extended period is crucial for a deeper understanding of the trends in biogeochemical developments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".