Automatic Framework for Extraction and Characterization of Wetting Front Propagation Using Tomographic Image Sequences of Water Infiltrated Soils
Bibliographic record
Abstract
This paper presents a new automatic framework for extracting and characterizing the dynamic shape of the 3D wetting front and its propagation, based in a sequence of tomographic images acquired as water (moisture) infiltrates in unsaturated soils. To the best of the authors' knowledge, the shape of the 3D wetting front and its propagation and progress over time has not been previously produced as a whole by methods in existing literature. The proposed automatic framework is composed two important and integrated modules: i) extraction of the 3D wetting front, and ii) characterization and description of the 3D wetting front to obtain important information about infiltration process. The 3D wetting front surface is segmented from 3D CT imagery provided as input via a 3D stochastic region merging strategy using quadric-regressed bilateral space-scale representations. Based on the 3D segmentation results, the normal directions at local curvature maxima of the wetting front surface are computed for 3D images of soil moisture, and its propagation is analyzed at the local directions in sites of maximal water adsorption, and described using histograms of curvature changes over time in response to sample saturation. These curvature change descriptors provide indirect measurements of moisture infiltration in soils, and soil saturation. Results using a field tomograph equipment specific for soil studies are encouraging, and suggest that the proposed automatic framework can be applied to estimate the infiltration of water in soils in 3D and in time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".