Colloquium 2016: Assessment of subsurface thermal conductivity for geothermal applications
Bibliographic record
Abstract
The construction of “green” buildings using geothermal energy requires knowledge of the ground thermal conductivity, assessed when designing the heating and cooling system of commercial buildings with ground-coupled heat pumps. The most commonly used method for active field assessment is the thermal response test (TRT), which consists of circulating heated water in a pilot ground heat exchanger (GHE) where temperature and flow rate are monitored. The transient thermal perturbation is analyzed to evaluate the subsurface thermal conductivity. Heat injection can also be performed with a heating cable in the GHE to conduct a TRT without water circulation, which can be affected by surface temperature variations. Passive methods, such as the interpretation of geophysical well logs and the analysis of temperature profiles measured in exploration wells, are emerging as alternatives to TRTs. Steady-state and transient laboratory measurements performed on samples collected in surface outcrops or drill cores can also be achieved. Methods to characterize the subsurface in the context of geothermal system design have evolved significantly since the original TRT concept proposed during the 1980s with different techniques inspired from the Earth science sector.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".