An Efficient Calibration Technique for Heat Dissipation Matric Water Potential Sensors
Bibliographic record
Abstract
Heat dissipation sensors are used to measure matric potential in soils. A van Genuchten equation can be used to fit the relationship between measured heat dissipation and matric potential. Calibration is required for each probe because of intrinsic variability in the properties of the porous material. However, calibration is time-consuming (months), requiring numerous measurements taken by a pressure plate apparatus over their operational range. Here the feasibility of minimizing the number of measurement points required to reliably characterize the calibration curve is explored. A two parameter (m = 1−1/n) and a three parameter (m is a free parameter) van Genuchten type model is used for the calibration. We explore how reducing the number of calibration measurement points impacts the resulting calibration curve, that is, what is the information content that each measurement provides, and how significantly is the calibration performance degraded by removing measurement points. We also consider how measurement errors during the calibration process, which are understood to be non-uniform over the range of matric potential values, result in uncertainty in the calibration curve, and explore how this uncertainty can be minimized. Different measurement locations (i.e., matric potential values) are found to contain different information content. A two-parameter-model calibration using four calibration points is recommended; and the specific location of these four points is essential to maximize the accuracy and the efficiency of the calibration. As a rule of thumb, the four points should be uniformly distributed on a log-scale over the pressure range from 20 to 1000 kPa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".