Column experiments to anticipate clogging of standing column wells
Bibliographic record
Abstract
Standing column wells are a promising solution to reduce the environmental footprint of building energy consumption.Nevertheless, as in other open-loop wells, they can be affected by clogging processes if detrimental hydrogeological conditions are present locally.This problem is relatively rare and still difficult to anticipate, even though some specific factors have been reported, such as substratum mineralogy and groundwater quality.This study proposes the use of a column experiments and coupon cell to anticipate clogging at two different sites near Montréal, Canada.The experiments were performed for duration of 50 and 52 d using thermoregulated columns at four temperatures.The results identified a difference in the chemistry of each site without any significant clogging risk.Site A showed a decrease in carbonates, magnesium, and calcium ions, and scanning electron microscopy showed a minor tendency to form carbonate deposits.Sulfate and calcium dissolution of the bedrock material were observed at Site B. Scanning electron microscopy of the coupons revealed organic matter with high carbon and sulfate concentrations.The same type of deposits was observed at Site B after three years of operation.In conclusion, these tests helped identify various potential clogging phenomena and indicated that both sites are not susceptible to major clogging risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".