The use of heat for subsurface flow quantification and understanding the subsurface heterogeneity
Bibliographic record
Abstract
The heterogeneity of subsurface media and variability of hydraulic conductivity controls groundwater flow and contamination transport in aquifers. Here, several complementary approaches are developed to used heat to better constrain subsurface flow and reconstruct the subsurface heterogeneity. A typical approach for reconstruction of hydraulic conductivity values and their spatial distribution is hydraulic tomography relying on the inversion of hydraulic head data. In this work, we first assess the advantages of groundwater flux data (inferred by using Active Fiber Optic Distributed Temperature Sensors (A-FO DTS)) in a synthetic hydraulic tomography test. Next, we design and implement the first hydraulic tomography experiment measuring groundwater head and flux data simultaneously in a shallow granular aquifer at Saint-Lambert site close to Québec city, Canada. The results from numerical studies reveal that flux data outperforms the head data for a small number of observations while for the high number of observations, the reconstructed heterogeneity is independent of the data type. The results from the hydraulic tomography experiment show the advantage of including a high number of measured flux obtained by A-FO DTS leads to a reduction of estimation uncertainty. Finally, the passive DTS is used to monitor groundwater flow in an artesian fractured borehole at Ploemeur site in Brittany. The results unveil the potential of passive DTS for capturing the groundwater dynamics in fractured media.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".