Space‐local spectral texture segmentation applied to characterizing the heterogeneity of hydraulic conductivity
Bibliographic record
Abstract
Spatial variability of hydraulic conductivity exerts a predominant control on groundwater flow by influencing advective pathways, hydrodynamic dispersion, and density‐dependent instabilities. Space‐local spectral texture segmentation aids in the macroscale characterization of the spatial heterogeneity of natural porous media via an outcrop analogue approach. Detailed photographic data sets were obtained for a 45 m × 3 m vertical section of glacial‐fluvial sand and gravel deposit in the Fanshawe Delta area (Ontario, Canada). High‐resolution texture maps of the sedimentary exposure are generated using a texture segmentation routine based on the space‐local S transform with the photographic data sets used as input. Geostatistical analyses of the texture maps reveal similarity between the spatial correlation structures of spectral texture and hydraulic conductivity as determined from constant‐head permeameter testing of sediment cores. Conditioned on the permeameter measurements, texture maps can be used to provide local continuous estimates of the hydraulic conductivity field at a spatial resolution equal to the sediment core dimensions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".