Capacitive conductivity logging and electrical stratigraphy in a high-resistivity aquifer, Boise Hydrogeophysical Research Site
Bibliographic record
Abstract
Abstract We tested a prototype capacitive-conductivity borehole tool in a shallow, unconfined aquifer with coarse, unconsolidated sediments and very-low-conductivity water at the Boise Hydrogeophysical Research Site (BHRS). Examining such a high-resistivity system provides a good test for the capacitive-conductivity tool because the conventional induction-conductivity tool (known to have limited effectiveness in high-resistivity systems) did not generate expressive well logs at the BHRS. The capacitive-conductivity tool demonstrated highly repeatable, low-noise behavior but poor correlation with the induction tool in the lower-conductivity portions of the stratigraphy where the induction tool was relatively unresponsive. Singular spectrum analysis of capacitive-conductivity logs reveals similar vertical-length scales of structures to porosity logs at the BHRS. Also, major stratigraphic units identified with porosity logs are evident in the capacitive-conductivity logs. However, a previously unrecognized subdivision in the upper portion of one of the major stratigraphic units can be identified consistently as a relatively low-conductivity body (i.e., an electrostratigraphic unit) between the overlying stratigraphic unit and the relatively high-conductivity lower portion — despite similar porosity and lithology in adjacent units. The high repeatability and resolution and the wide dynamic range of the capacitive-conductivity tool are demonstrated here to extend to high-resistivity, unconsolidated sedimentary aquifer environments.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".