Integrated Sensor for Estimating in situ Soil Water Content in Vertical Profile
Bibliographic record
Abstract
Many agricultural and hydrological processes require the detailed knowledge of soil water content (SWC) in the vertical profile. Quantifying real-time and in situ SWC is difficult due to time, cost, toil, and technical issues. This paper describes the development of a multi-depth SWC monitoring sensor which can estimate the SWC from 4 vertical depths simultaneously. The probe is a type of electromagnetic (EM) sensor that indirectly measures the SWC on the basis of dielectric theory. The sensor was calibrated with soil samples of three distinct topographical locations. The calibration models were established by fitting linear order equations. The performance of the sensor was evaluated in situ field conditions. A multi-depth SWC curve was investigated to examine the impact of continuous estimations of SWC at specified depths on the sensor performance. The sensor was integrated with vertical interpolation technique to improve the measurement accuracy. The results indicated the optimal range of the SWC measurements, and the estimation error was less than 5%, except irrigation cycles. The linear fit coefficient of determination (R2) ranged from 0.957 to 0.993 and root mean square error (RMSE) was ranging from 1.565 to 4.456. The results showed that the sensor performed consistently better for at least 4 months within acceptable soil conditions. The sensor will be advantageous for continuous estimations of SWC, and managing the irrigation practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".