Time domain reflectometry for water content and density of soils: study of soil-dependent calibration constants
Bibliographic record
Abstract
The paper studies the soil-dependent calibration constants used for determining water content and density of soil using time domain reflectometry (TDR), specifically, to establish the typical soil calibration values and study the extent of the uncertainty in calibration factors on measurement accuracy. The TDR method described here makes use of a calibration equation normalized by soil dry density, which involves two soil-dependent constants, a and b. Both a and b have physical significance, with the value of a related to the apparent dielectric constant of the dry density – normalized dry soil solids and the value of b related to the apparent dielectric constant of the pore fluid. From theoretical predictions, typical values of a are around 1.0, and typical values of b are around 9. Practically, the constants a and b are obtained through calibration tests performed in conjunction with standard compaction tests. Experimental study shows that calibration constants fall within the ranges from theoretical predictions. Tests on five soil mixtures provided average values of a = 0.945 and b = 8.76, while 11 clean sands resulted in average values of a = 1.0 and b = 8.5. The study also shows that there are no significant effects of compaction energy on the measured values of a and b. Sensitivity analyses indicate that variations in a and b both cause variations in TDR-determined water content and density, but the variations are typically within acceptable limits for engineering application purpose. Results from TDR tests on simulated field experiments are consistent with the sensitivity analyses.Key words: time domain reflectometry, TDR, calibration constants, water content, dry density, sensitivity.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".