Factors affecting soil permittivity and proposals to obtain gravimetric water content from time domain reflectometry measurements
Bibliographic record
Abstract
Time domain reflectometry (TDR) measures the apparent relative dielectric permittivity (ARDP) of a soil and is commonly used to determine the volumetric water content (VWC) of the soil. ARDP is affected by several factors in addition to water content, such as the soil’s electrical conductivity, temperature, and density. These relationships vary with soil type and are very soil-dependent, and despite previous research, they are still not fully understood. A multivariate statistical approach (principal component analysis, PCA) is used to describe a range of soils from two separate sites in the UK (clay and silty sand – sandy silt). The advantage of a PCA is that it considers several variables at a time, giving an immediate picture of their underlying relationships. It was found that for the studied soils, ARDP was positively correlated with VWC and bulk electrical conductivity, but did not show any dependence on some other geotechnical parameters. TDR has recently been used in geotechnical engineering for measuring the gravimetric water content (GWC) and dry density. However, the current approaches require a custom-made TDR probe and an extensive site specific empirical laboratory calibration. To extend the potential use of TDR in the geotechnical industry, three relatively simple methods are proposed to estimate the GWC from VWC (derived from the measured ARDP values) and dry density depending on the amount of information known about the soil. Examples of possible applications of these methods include continuous monitoring of consolidation adjacent to a structure, the effect of seasonal weather and climate change on ageing earthwork assets, and the shrink–swell potential adjacent to trees. All three methods performed well, with between 83% and 98% of the data lying within a ±5% GWC envelope, with the data for clay soils performing better than those for silty sands – sandy silts. This is partly due to the fact that the applied relationship converting ARDP to VWC performs better for clays than silty sands – sandy silts, as well as less variation of the estimated bulk density that is needed to derive the dry density.
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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 | 0.001 | 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 | 0.001 |
| 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".