Observability analysis for soil moisture estimation ⁎ ⁎Natural Sciences and Engineering Research Council, Canada.
Why this work is in the frame
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Bibliographic record
Abstract
The knowledge of soil moisture is important in studying climatology, earth science and most importantly irrigation decision support systems, but is often hard to determine since it is not possible to use critical measurements including moisture sensors all over the entire agricultural grid sector. As a result, soil moisture at unmeasured region needs to be estimated, which can be done using state estimators such as Kalman based estimators. The model that is used to represent water transfer between atmosphere, plant and soil, also known as agro-hydrological model, is highly nonlinear. Since 'strong' rather than 'weak' observability of the system ensures better performance of Kalman based estimators to develop a reliable soil moisture estimation algorithm, the main objective of this study is to discuss observability analysis of this nonlinear agro-hydrological system. The study was performed using synthetic data. The extended Kalman filter (EKF) was chosen as the state estimator. As would be expected, the results show that the EKF performance is better in cases where the system is 'strongly' observable.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it