Hydrostratigraphic Characterization of Glaciofluvial Deposits Underlying an Infiltration Basin Using Ground Penetrating Radar
Bibliographic record
Abstract
An understanding of the heterogeneity of quaternary gravelly deposits is required to predict flow and contaminant transfer through these formations. In such deposits, preferential flow paths can lead to contamination at depths greater than predicted under the assumption of a homogeneous medium. The difficulties in characterizing their complex structure with conventional methods represent an obstacle for this prediction. In this study, we developed an approach relying on the use of ground penetrating radar (GPR) for the detection of sedimentary depositional units. A genetic interpretation of the radar stratigraphy allowed us to construct a distribution model of lithofacies. The study was conducted on glaciofluvial deposits underlying a stormwater infiltration basin. Two main system tracts were characterized: a top stratum (50–80 cm deep) corresponding to massive gravel and open‐framework gravel, and a base stratum corresponding to trough‐fill structures with associated sandy, open‐framework, massive, and matrix‐rich gravelly lithofacies. The knowledge of the hydraulic properties linked to each lithofacies led us to propose a hydrostratigraphic model. Based on this model, we formulated a hypothesis about the hydraulic behavior of the deposit during stormwater infiltration. Open‐framework gravels can act, during complete saturation, as preferential flow paths, and capillary barrier effects may occur under variably saturated conditions. These hypotheses were tested by measuring water content variations (using time domain reflectometry probes) at three depths (0, −0.5, and −1.15 m). Experimental data show infiltration behavior that can be explained by a capillary barrier effect between the two lower probes. These results suggest that our hypothesis about hydraulic behavior is reasonable.
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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 | 0.000 |
| 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) | 0.000 | 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".