Calibration and Evaluation of a Frequency Domain Reflectometry Sensor for Real‐Time Soil Moisture Monitoring
Bibliographic record
Abstract
Soil spatial heterogeneity poses a challenge to accurate soil moisture determination. Remote sensing, in particular, using sensors that acquire data at microwave frequencies, is being used to overcome this challenge. In situ soil moisture monitoring can be used to validate remotely sensed surface soil moisture estimates and as inputs for agronomic and hydrologic models. Nine in situ soil moisture stations were established in Manitoba (Canada) and instrumented with Stevens Hydra Probes. The sensors were installed in triplicate with vertical orientation at the surface and with horizontal orientation at the 5‐, 20‐, 50‐, and 100‐cm depths. To ensure accuracy of the measured soil moisture, both laboratory and field calibrations were conducted. These calibrated soil moisture values were compared with the probe default values and those generated using published calibrations. Overall, the results showed that the field calibration was superior (coefficient of determination r 2 of 0.95) to the laboratory calibration ( r 2 of 0.89). In addition, coarse‐textured sites generally performed better than the fine‐textured, high cation exchange capacity (CEC) sites. At the Kelburn site with high clay and CEC, the use of field calibration reduced the root mean square error from 0.188 to 0.026 m 3 m −3 . However, at the low clay and CEC Treherne site, gains in accuracy were minimal, about 0.005 m 3 m −3 . The laboratory calibration consistently underestimated soil moisture at all the evaluation sites, whereas both Topp and Logsdon calibrations overestimated soil moisture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".