Characterization of soil moisture conditions at temporal scales from a few days to annual
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This work proposes the analysis of soil moisture conditions based on the use of two recently developed descriptive techniques: (1) wavelet analysis and (2) self‐organizing mapping through Kohonen neural networks. This analysis is applied to soil moisture profiles as well as supporting data, i.e. precipitation, temperature and flow observations, from an experimental site in the Orgeval watershed in France. With wavelet analysis and self‐organizing mapping, a comprehensive description of the structure of soil moisture profile, its evolution over time, and how it relates to observations of precipitation, temperature and flow can be obtained. Soil moisture conditions, particularly in the Orgeval watershed, are an important feature of the hydrologic cycle. There might be a significant advantage to consider soil moisture information in a variety of hydrologic models, such as streamflow models often employed in simulation and prediction modes for operational purposes, and the analysis performed here provides some avenues leading to the consideration of this information. Copyright © 2004 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) | 0.002 | 0.001 |
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