AUTOMATIC <i>IN SITU</i> DETERMINATION OF FIELD CAPACITY USING SOIL MOISTURE SENSORS
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Bibliographic record
Abstract
ABSTRACT Field capacity is a frequently used concept in irrigation systems and agriculture. Although there is some debate on how field capacity is defined, it is generally accepted as being the upper limit on the available water that is stored in a soil profile that can be held against the pull of gravity. Standard methods for determining field capacity require waiting two days before measuring volumetric water content after raising the water content above field capacity. This is time‐consuming and subject to natural water inputs during the monitoring period. The contribution of this work is a method for rapidly estimating field capacity in situ using volumetric water sensors, a specific irrigation schedule, and a nonlinear curve fitting model that predicts field capacity using a drainage model. The result allows for determining field capacity rapidly with very good accuracy and can be used with automatic irrigation systems in landscaping and agriculture. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 it