Thermal conductivity of soils by weighted average model with transitional air/water inter-phase
Bibliographic record
Abstract
This study presents two straightforward weighted average models for estimating the thermal conductivity of unsaturated soils. The first model ( WAM TAWI -1 ) incorporates the weighted average contributions from primary soil constituents (i.e., quartz ) and residual soil minerals , which are surrounded by a single continuous phase ( a transitional air/water inter-phase fluid ) with continuously changing thermal conductivity ( λ ) within its boundary values representing air ( λ a ) and water ( λ w ) . Similarly, the second model ( WAM TAWI -2 ) incorporates the weighted average contributions from all soil minerals and the transitional air/water inter-phase fluid. In contrast to the original model by de Vries in 1963, these models are free of complex latent heat transfer expressions, water/air shape fitting factors, and critical water content for changing continuous medium between air and water; consequently, they are exceptionally easy to use. The WAM TAWI -1 model is recommended because it only requires commonly available data: soil porosity , grain size distribution , and quartz content, while the WAM TAWI -2 model requires full mineral composition of a soil which is rarely available. The WAM TAWI -1 model was effectively validated with respect to λ data of 39 Canadian field soils, three standard sands, and 10 Chinese soils. The average standard deviations ( SD ) were ± 0.093 W⋅m −1 ⋅K −1 for 17 coarse soils, ±0.068 W⋅m −1 ⋅K −1 for 22 fine soils, and ± 0.079 W⋅m −1 ⋅K −1 for all Canadian soils. For the three standard sands, the average SD was ±0.143 W⋅m −1 ⋅K −1 , demonstrating good performance. Among the 10 Chinese soils, the model demonstrated good performance of ±0.142 W⋅m −1 ⋅K −1 for five coarse soils, while delivering superior estimates of ±0.094 W⋅m −1 ⋅K −1 for the remaining five fine soils. The results confirm the outstanding superiority of the model with respect to the original model by de Vries, in terms of its simplicity and λ predictions.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".