Laboratory Calibration Procedures of the Hydra Probe Soil Moisture Sensor:Infiltration Wet-Up vs. Dry-Down
Bibliographic record
Abstract
Impedance probes are popular electromagnetic soil moisture monitoring devices used for a variety of applications but require site-specific calibrations to provide accurate measurements. Several calibration techniques have been reported in the literature, although laboratory-based procedures involving wet-up (via upward or downward infiltration) and dry-down are commonly performed for permanently installed sensors. Wet-up calibrations can be completed substantially faster (<1 d) than dry-down calibrations (1–2 wk), but it is uncertain which technique is preferable to provide the most accurate calibration. The objective of this study was to compare the results obtained from laboratory-based infiltration wet-up and dry-down calibrations of the Stevens Hydra Probe soil moisture sensor. Soil samples for this study were obtained from agricultural sites in Saskatchewan, Canada, at depths of 5, 20, and 50 cm across a variety of textural compositions. Results demonstrate that utilizing either infiltration wet-up (according to the procedure in this study) or dry-down procedures provides accuracies of <0.061 m3 m−3 root mean square error (RMSE), which was superior to manufacturer calibration accuracy across all samples. However, superior calibration accuracies (i.e., the lowest RMSE) were achieved using the dry-down procedure across all soil samples, resulting in a lower RMSE of 0.01 to 0.04 m3 m−3 (at 95% confidence). A significant correlation (r value = 0.61, p < 0.05) exists between the differences in infiltration wet-up and dry-down calibration RMSEs and clay content. This suggests that the difference between the two procedures tested in this study is more significant in finer textured soils. The findings of this study indicate that the dry-down procedure produced the lowest RMSE and is therefore the preferred calibration procedure, particularly for finer textured soils.
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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".