Laboratory and field evaluation of five soil water sensors
Bibliographic record
Abstract
Accurate in situ determination of soil water content is important in many fields of agricultural, environmental, hydrological, and engineering sciences. As numerous soil water content sensors are available on the market today, the knowledge of their performance will aid users in the selection of appropriate sensors. The objectives of this study were to evaluate five soil water sensors in the laboratory and to determine if laboratory calibration is appropriate for the field. In this study, the performances of five sensors, including the Profile Probe™ (PP), ThetaProbe™ , Watermark™, Aqua-Tel™, and Aquaterr™ were compared in the laboratory. The PP and ThetaProbe™ were more accurate than the other soil water sensors, reproducing soil water content using factory recommended parameters. However, when PP was installed on a loamy sand in the field, the same soil that was used for the laboratory evaluation, it overestimated field soil water, especially at depth. Another laboratory experiment showed that soil water content readings from the PP were strongly influenced by soil bulk density. The higher the soil bulk density, the greater was the overestimation of soil water content. Two regression parameters, a0 and a1, which are used to convert the apparent dielectric constant to volumetric water content, were found to increase linearly with the soil bulk density in the range of 1.2 to 1.6 Mg m-3. Finally, the PP was calibrated in the field and a good calibration function was obtained with an r2 of 0.87 and RMSE of 2.7%. The values of a0 and a1 obtained in the field were different from factory recommended parameters (a0 = 2.4 versus 1.6 while a1 = 12.5 versus 8.4) and were independent of soil depth, bulk density, and texture. As such, individual field calibration will be necessary to obtain precise and accurate measurement of soil water content with this instrument. Key words: Soil water content, Profile Probe, calibration, soil water content sensor
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".