An improved model and field calibration technique for measuring liquid water content in unfrozen and frozen soils with dielectric probes
Bibliographic record
Abstract
Abstract Dielectric soil moisture probes can be used to obtain frequently logged field observations of volumetric water content, but because this is an indirect method, it is challenging to ensure that you have a suitable calibration relationship and parameters. Challenges are associated with (a) limitations in the probes’ ability to accurately measure the soil bulk dielectric constant and (b) limitations in obtaining reliable direct observations to calibrate the probe against, due to the spatial heterogeneity in the field. Furthermore, in soils that freeze, we do not have a robust approach to account for the effect of ice on the instrument. In this study, we propose a calibration relationship for all dielectric probes that is physically based, parsimonious, accounts for errors in the bulk dielectric constant, and quantifies the uncertainty in the liquid water content caused by the presence of ice. We show that our relationship has a better performance and more realistic parameter values than existing approaches. To calibrate a dielectric probe in the field, we show that it is necessary to account for spatial heterogeneity in samples taken for calibration. There is value in taking simultaneous measurements of the bulk dielectric constant (using the same dielectric probe as is being calibrated) and gravimetric samples for water content at a number of points in space. Taking too few samples is likely to be misinformative—we show that it is better to not calibrate the probe at all than to calibrate it with too few data points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".