NUMERICAL ANALYSIS OF HEAT TRANSPORT WITHIN FRACTURED SEDIMENTARY ROCK: IMPLICATIONS FOR TEMPERATURE PROBES
Bibliographic record
Abstract
Thermal energy transport from ground surface into a fractured sedimentary rock aquifer is simulated numerically to gain insight into the response of temperature probes for identifying hydraulically active fractures in rock boreholes. The conceptual hydrogeological model is based on a field study site at Guelph, Ontario, Canada, where thin sandy overburden overlies a densely-fractured dolostone. Characterizing the fracture network is important in this area for understanding the behaviour of DNAPL contaminants at nearby industrial sites. The model includes density-dependent groundwater flow coupled with thermal advection, conduction, and retardation within the porous matrix and discrete fractures. The fracture elements are generated stochastically and are coupled directly with the porous medium matrix blocks. Natural background flow gradients, surface recharge and seasonally variable surface temperatures are also considered. The results show that ground source thermal pulses can propagate deep into a fractured rock system and appear as weak thermal “anomalies” within the fractures on the order of a few tenths or hundredths of degrees. Such anomalies are now becoming detectable with state-of-the-art thermal probe technologies and can be potentially valuable natural signals for identifying hydraulically active fracture zones.
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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 | 0.002 |
| 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 | 0.001 | 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".