Calibration of thermal conductivity sensors with consideration of hysteresis
Bibliographic record
Abstract
A thermal conductivity sensor monitors soil suction by measuring the changes in thermal conductivity of the porous tip. The thermal conductivity of the porous tip is a direct function of its water content. It has long been recognized that the suction versus water content relationship of a porous material exhibits hysteresis of various magnitudes between wetting and drying processes. The sensor output may correspond to various suction values of the sensor porous tip, depending on the wetting or drying state of the porous tip. The current calibration procedure, however, represents only one of the drying processes of the sensor porous tip. A laboratory testing program was carried out to better understand the hysteretic properties of the sensor output voltage versus the suction and to further improve the calibration procedure. The output of the sensor was monitored as the sensor porous tip was subjected to various drying and wetting processes. The test results indicate an error of 30%70% for suctions higher than 100 kPa if the hysteretic effects of the porous tip are not considered in data interpretation. Based on the laboratory testing results, a revised calibration procedure was proposed that takes into consideration the capillary hysteretic effects.Key words: thermal conductivity sensor, calibration, matric suction, capillary hysteresis.
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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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".