Simultaneous Estimation of Soil Moisture and Hydraulic Parameters for Precision Agriculture. Part B: Application to a Real Field
Bibliographic record
Abstract
In the first part of this work [1], we presented the methodology for simultaneous soil moisture and hydraulic parameter estimation. In [1], sensitivity analysis and orthogonalization projection were used to select a subset of the most estimable states and hydraulic parameters at every sampling time, an extended Kalman filtering approach that can take into account the variable selection results was also presented. The methodology was demonstrated to be effective using a simulated case study.In the second part of this work, we further apply the simultaneous estimation method to a real large-scale agriculture field in Lethbridge, Alberta, Canada. The field is equipped with a centre pivot irrigation system and microwave soil moisture sensors are mounted on the centre pivot. Cross-validation results obtained for two specific days revealed that the state and parameter estimation procedure improved the accuracy of the soil moisture estimates by 24% and 43%.
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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".