Theory and Solutions of Heat Pulse Method for Determining Soil Thermal Properties
Bibliographic record
Abstract
Information on thermal properties of soil is of paramount importance for environmental and earth science, and engineering. The heat pulse (HP) method has become the key technology for accurate determination of soil thermal properties and a variety of other physical properties (e.g., water content, bulk density, and water flux) in both laboratory and field environments. The HP method is a transient method that is commonly based on the analytical solutions to the radial heat flow equation when a line-heat source is applied. Over the past few decades, great endeavors have been devoted to advance the HP method. For example, the evolution and development in probe design, data logging equipment, data interpretation and computing capability has remarkably improved the accuracy and ease of use for determining soil thermal properties. However, there is a lack of study collating and synthesizing the development of the theory/solutions to obtain thermal properties of soil using the HP method. In this paper, we review the fundamental theories and solutions of the HP method, including differences and similarities of theories and applications between instantaneous line heat source (ILHS) and short-duration line heat-source (SLHS), between dual-probe heat-pulse (DPHP) and single-probe heat-pulse (SPHP) methods, and between the non-linear model fit (NMF) method and single point (SPM) method for data interpretation. In addition, the numerical solutions and semi-analytical solutions are also presented to provide heat pulse users information for selecting the best-fit method to meet their goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".