Abstract of "AN EXPERT SYSTEM FOR ESTIMATING SOIL THERMAL AND TRANSPORT PROPERTIES"
Bibliographic record
Abstract
Prediction of soil thermal conductivity k is particularly difficult at high temperatures T (50-90 °C) and very low moisture content θ; the k value may increase with temperature by a factor of 3-5. This phenomenon is due to water vapor migration resulting in latent heat transfer, which is strongly dependent on soil water characteristics (SWC). In the past, the SWC influence on k prediction was never studied in great detail - mainly due to a lack of reliable SWC experimental data at low θ. Currently, hydraulic properties of soils can be evaluated from numerous predictive models correlated with experimental data. The paper objectives focus on: effects of SWC on k prediction; explanation of nonlinear variation of k at high T with θ ranging over the full degree of saturation; large k over- predictions at low θ and high T. Results obtained show very strong k dependence on the SWC function; therefore, accuracy of SWC estimates cannot be disregarded. The paper explains a nonlinear k behavior at high T as a combined effect of water vapor migration, SWC, and soil air relative humidity. To this end the paper provides also information about Soil Thermal and Transport Properties — Expert System (STTP-ES), collection of the notable predictive models for these properties, for both moist and frozen soils, which have been gleaned from the literature and integrated into one software package. The appendix provides a brief description and recent modifications made to the STTP-ES. Closing discussion concentrates on STTP-ES shortcomings, development of the Windows driven package, Expert System extension to other porous media and the database development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".