Monitoring water flow in a clay-shale hillslope from geophysical data fusion based on a fuzzy logic approach
Why this work is in the frame
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Bibliographic record
Abstract
Seismic and electrical resistivity tomography allow subsurface characterization from acoustic P-waves (Vp), shear S-waves (Vs) velocities, and electrical resistivity (ρ). Both geophysical methods were used to monitor water flow during a controlled rainfall experiment on a clay-shale hillslope located in the Laval catchment at Draix (Alpes-de-Haute-Provence, France). The objectives of the rainfall experiment were to analyse the water infiltration processes and identify possible water pathways by combining multi-method observations. The seismic data provide information on fissure density and the electrical resistivity data provide information on soil water content within the hillslope. Changes of the Vp and electrical resistivity fields with time show some similar pattern. To go further in the analysis of the water flow a geophysical data fusion strategy based on fuzzy set theory is applied. The computed fuzzy cross-sections based on expert hypotheses show the possibility for the material to be saturated during the rainfall experiment. The data fusion process is repeated in time for each acquisition set. The relative difference between the obtained fuzzy cross-sections is calculated and reveals possible locations where water may be transferred within the hillslope.
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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 | 0.001 | 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