Using Direct‐Push EC Logging to Delineate Heterogeneity in a Clay‐Rich Aquitard
Bibliographic record
Abstract
Abstract Heterogeneity exerts an important control on solute transport pathways in any subsurface environment, including aquitards where diffusion is the dominant transport mechanism. Direct‐push (D‐P) electrical conductivity (EC) logging has recently enabled high‐resolution hydrostratigraphic characterization of predominantly coarse‐grained sediments in low‐salinity aquifers. In this paper, we apply D‐P EC logging to characterize the spatial variability of physical and chemical properties in a clay‐rich aquitard that has known vertical contrasts in pore water salinity. The D‐P EC logging was conducted to a maximum depth of 17.5 m below ground (BG). Results obtained from 22 D‐P EC logs across a 140‐ × 80‐m field site were compared with pore water chemistry data from squeezed core samples ( n = 21) and piezometers ( n = 6) to reveal complex spatial variations in pore water salinity ranging from <5000 to ∼90,000 μS/cm. The EC distributions appear to be controlled by nonuniform salt fluxes from the unsaturated zone to the water table and subsequent downward diffusion. Detailed D‐P EC logging also revealed the presence of a single discontinuous sand lens located between 9.4 and 15.9 m BG that may have important controls on solute transport pathways. The only limitation with using this method in very fine‐grained sediments is that depths of tool penetration may be considerably less than those currently achievable in sand and silt deposits.
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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 | 0.001 | 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".