Time domain reflectrometry measurements of water content in coarse waste rock
Bibliographic record
Abstract
Methods are presented to calibrate and implement a time domain reflectrometry (TDR) system to estimate the water content of coarse mine waste rock containing high solute concentrations in the pore water. High electrical conductivity of the pore water reduces the quality of TDR waveforms through the loss of signal amplitude. Zegelin-type probes with a resistive coating on the center conductor were used to obtain higher signal-to-noise waveforms. These probes must be calibrated prior to use. The TDR pulse travel-time measured in soils containing pore water with high solute concentrations increases systematically with solute concentration. Empirical calibrations are derived for waste rock with low and high electrical conductivity pore water. An ambient temperature correction is derived from observed diurnal fluctuations in the measured travel time to determine whether or not a detailed laboratory calibration for temperature effects is required. The variation of apparent dielectric permittivity with temperature is positively correlated with temperature at low water content and negatively correlated at high water content. This trend indicates the influence of water bound to mineral surfaces on the variation of apparent dielectric permittivity with temperature. Examination of our field data indicates that the effect of dissolved ions on the TDR calibration was great enough to justify a calibration that accounts for pore-water composition. The effect of ambient temperature was found to be small enough that an intensive laboratory calibration was not required.Key words: time domain reflectrometry, water content, mining.
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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) | 0.001 | 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".