Long-term measurement of matric suction using thermal conductivity sensors
Bibliographic record
Abstract
Thermal conductivity (TC) sensors, which provide estimates of matric suction, were used in a field experiment designed to characterize unsaturated water movement through coarse mine waste rock at a mine site in northern Saskatchewan. Two years of monitoring data were used to evaluate long-term TC sensor performance and accuracy. Thermal conductivity sensor output requires corrections of sensor hysteresis and changes in ambient temperature. A correction method for ambient temperature is derived. A comparison of the uncorrected field measurements with the values corrected for both hysteresis and ambient temperature indicates that the magnitude of these corrections can be similar. Corrected TC sensor measurements are compared to measurements of matric suction made using tensiometers. Thermal conductivity sensor response to the initial arrival of a wetting front lagged 13 days behind the tensiometer measurements. The TC sensor data tended to overestimate matric suction in the waste rock, when compared to the tensiometer data. Long-term drift in the TC sensors located at depths of 50 cm and below (where the sensors have been continuously exposed to matric suctions less than 20 kPa) has lead to data that are not interpretable using the calibration curves derived prior to sensor emplacement in the waste rock pile.Key words: matric suction, thermal conductivity sensor, hysteresis, temperature.
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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 | 0.000 | 0.001 |
| 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 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".