Radar Determination of the Spatial Structure of Hydraulic Conductivity
Bibliographic record
Abstract
Spatial variability of hydraulic conductivity exerts a predominant control on the flow of fluid through porous media. Heterogeneities influence advective pathways, hydrodynamic dispersion, and density-dependent dispersion; they are, therefore, a key concern for studies of ground water resource development, contaminant transport, and reservoir engineering. Ground-penetrating radar contributes to the remote, geophysical characterization of the macroscale variability of natural porous media. On a controlled excavation of a glacial-fluvial sand and gravel deposit in the Fanshawe Delta area (Ontario, Canada), the hydraulic conductivity field of a 45 x 3 m vertical exposure was characterized using constant-head permeameter measurements performed on undisturbed horizontal sediment cores. Ground-penetrating radar data were collected along the excavation face in the form of both reflection and common midpoint surveys. Comparison of geostatistical analyses of the permeameter measurements and the radar data suggests thatthe horizontal correlation structure of radar stack velocity can be used to directly infer the horizontal correlation structure of hydraulic conductivity. The averaging nature of the common midpoint survey is manifest in the vertical correlation structure of stack velocity, making it less useful. Radar reflection data do not exhibit a spatial structure similar to that of hydraulic conductivity possibly because reflections are a result of material property contrasts rather than the material properties themselves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".