The Effects of Salinity on the Accuracy and Uncertainty of Water Content Measurement
Bibliographic record
Abstract
We used an automatic network analyzer (ANA) operated in both time and frequency domain modes to investigate the measurement accuracy of metallic time domain reflectometry (TDR) probes operated in sands saturated with NaCl solutions of varying electrical conductivity (EC). We chose to use time domain transmission (TDT) measurements for this investigation to separate the effect of the bulk soil‐probe interaction from the effect of the large reflection typically found at the air–soil boundary for a TDR configuration. Pulse travel times and their variability increase with increasing pore‐water EC. The source of travel time variability arises from the extreme variability in pulse shape, thereby introducing a high degree of uncertainty to curve fitting routines used to determine travel times. Pulse shape distortion is due primarily to attenuation of high frequency components through conductive loss rather than by dispersion. There is generally good correspondence between pulse rise time and the average water content measurement error over a 0‐ to 40‐dS m −1 EC range. For rise times <6 ns, measurement errors are <0.1 m 3 m −3 Given that rise times can be determined easily, we recommend that they be reported routinely as indicators of data quality. Because the variability of travel time measurements at a given EC is on the same order as the average difference between the travel time measured at that EC and that measured at zero salinity, pulse rise times cannot be used to correct for individual travel time measurements at high salinity. Similarly, knowledge of the EC of the medium may allow for identification of erroneous water content measurements, but does not allow for error correction.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".