Laboratory calibration of time domain reflectometry to determine moisture content in undisturbed peat samples
Bibliographic record
Abstract
Time domain reflectometry (TDR), while widely used to measure volumetric water content ( θ ) and bulk electrical conductivity (BEC) in unsaturated granular soils, remains less studied in peat than mineral soils. Empirical models commonly used in mineral soils are not applicable to peat for accurate determination of θ from measured apparent dielectric permittivity ( ɛ ). Past studies for peat report highly variable calibrations, and suggest differences in origin of organic matter, degree of decomposition and bound water to explain such variability. This study shows that bound water appears to have minimal impact on calibration because of its negligible volumetric fraction at the low bulk densities of peat. Increased volumetric air fraction at the same θ values attributed to high porosity of peat makes the ɛ ‐ θ relationships of mineral soils inapplicable. Temperature effects on ɛ resulted in a correction factor for θ . The temperature correction factor decreased with decreasing θ and was determined experimentally to lie between −0.0021 m 3 m −3 per °C for θ ≥ 0.79 m 3 m −3 and −0.0005 m 3 m −3 per °C for θ = 0.35 m 3 m −3 . The decreasing value of the correction factor with θ can be explained by dependence of the ɛ ‐ θ relationship on properties of free water alone. Temperature dependence of BEC was close to that of soil solution. Maxwell‐De Loor's four‐phase mixing model (MDL) based on physical properties of the multiphase soil system can efficiently simulate the effect of increased air volume and varying soil temperature on the ɛ ‐ θ relationship in peat. In addition, linear ɛ ‐ θ calibration in peat can be improved when BEC is included in the calibration equation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".