Single‐Probe Heat Pulse Method for Soil Water Content Determination: Comparison of Methods
Bibliographic record
Abstract
Core Ideas Six methods to obtain soil water content by single probe are presented and compared. Errors of estimated soil water content by six methods increase with water content. Heating duration affects soil water content estimation errors. Four of the six methods are probe dependent but can be easily calibrated. Each method works for some soils, and combining different methods is a solution. The estimation of soil thermal conductivity (λ) using the single‐probe heat pulse (SPHP) method is well known, but estimation of soil water content (θ) using the SPHP is poorly understood. In this study, we examined six methods—λ, normalized cumulative temperature increase (TN cum ), normalized maximum temperature increase (TN max ), and the reciprocals of each—for θ estimation using the SPHP. The temperature response curves of four soils at different θ were measured following 600‐s heat pulses with heating strengths of about 6 W m −1 , from which λ, TN cum , and TN max values were determined. The maximum measurement errors of these three methods were 0.11 m 3 m −3 for the coarse sand and 0.01 m 3 m −3 for the fine sand, sandy loam, and silty clay, except for 0.05 m 3 m −3 for the fine sand by the λ(θ) method. The predicted θ from all of the λ, TN cum , and TN max methods agreed well with that from the oven‐dry method for all soils with the exception of the TN cum (θ) and TN max (θ) methods for the coarse sand for θ > 0.20 m 3 m −3 . The measurement errors and θ predictions of the 1/λ(θ) method were similar to that of the TN cum (θ) and TN max (θ) methods, and that of the 1/TN cum (θ) and 1/TN max (θ) methods were similar to that of the λ(θ) method. Because each of the six methods worked well for only some soils, improved estimations were obtained when the λ(θ) method was combined with the 1/TN cum (θ) [or 1/TN max (θ)] method for coarse‐textured soils and the 1/λ(θ) method was combined with the TN cum (θ) [or TN max (θ)] method for fine‐textured soils.
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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 | 0.000 |
| 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".