Thermal conductivity of base-course materials
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the results of a comprehensive laboratory study on the thermal conductivity of dense and broadly graded coarse base-course materials used in pavements. Materials were selected from eight quarries along the axis of the St. Lawrence River to include a variety of samples of different geological origins. Nearly 200 tests were performed in a thermal conductivity cell using Pyrex heat flux meters to characterize the relationships between the thermal conductivity of unfrozen and frozen samples and the waterice content. Sixteen tests were also performed on solid rock cylinders to characterize the influence of mineralogy on the thermal conductivity of solid particles from the selected quarries. The most widely used empirical prediction models for thermal conductivity of soils from the literature were found inappropriate to estimate the thermal conductivity of base-course materials. An improved model using the geometric mean method to compute the thermal conductivity for the solid particles and the saturated materials, a modified form of the geometric mean method to predict the thermal conductivity of dry materials, and empirical relationships to assess the normalized thermal conductivity of unfrozen and frozen base-course materials are presented. This new model predicted well the thermal conductivity for more than 150 unfrozen and frozen coarse sand and gravel samples from the literature. A step by step methodology is proposed to assess the thermal conductivity of base-course materials.Key words: base course, porosity, degree of saturation, mineralogy, unfrozenfrozen, thermal conductivity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.053 | 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 it