Characterizing the Two‐Dimensional Thermal Conductivity Distribution in a Sand and Gravel Aquifer
Bibliographic record
Abstract
Both hydrologic and thermal transport properties play a significant role in the movement of heat through permeable sedimentary material; however, the thermal conductivity is rarely characterized in detailed spatial resolution. As part of a study of the movement of thermal plumes through a sand and gravel aquifer, we have constructed a two‐dimensional profile of thermal conductivity. This work consisted of: (i) measuring the thermal conductivity of the soil solids, λ s , for the main stratigraphic units using the steady‐state divided‐bar apparatus and estimating conductivity from mineral composition; (ii) measuring the volumetric water content and porosity using crosshole ground‐penetrating radar; (iii) evaluating four models used to predict the apparent thermal conductivity, λ, of variably saturated soils and selecting the best model using the information‐theoretic approach, (iv) calculating the λ field on a 0.25‐m square cell grid using measured data and the selected model, and (v) simulating thermal transport within the two‐dimensional domain using a finite element numerical model. The apparent thermal conductivity in the saturated aquifer ranges from 2.14 to 2.69 W m −1 K −1 with a mean of 2.42 W m −1 K −1 Numerical simulations show that the heterogeneous thermal conductivity field results in increased thermal dispersion that is most pronounced at the plume front. Our values for λ and λ s may be used for glacial soils with similar mineralogy and texture. Our methods may also be used at other sites to construct the thermal conductivity distribution.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".