In cooperation with the University of Waterloo, Canada Application of Advanced Geophysical Logging Methods in the Characterization of a Fractured-Sedimentary
Bibliographic record
Abstract
An integrated suite of advanced geophysical logging methods was used to characterize the geology and hydrology of three boreholes completed in fractured-sedimentary bedrock in Ventura County, California. The geophysical methods included caliper, gamma, electromagnetic induction, borehole deviation, optical and acoustic televiewer, borehole radar, fluid resistivity, temperature, and electromagnetic flowmeter. The geophysical logging 1) provided insights useful for the overall geohydrologic characterization of the bedrock and 2) enhanced the value of information collected by other methods from the boreholes including core-sample analysis, multiple-level monitoring, and packer testing. The logged boreholes, which have open intervals of 100 to 200 feet, penetrate a sequence of interbedded sandstone and mudstone with bedding striking 220 to 250 degrees and dipping 15 to 40 degrees to the northwest. Fractures intersected by the boreholes include fractures parallel to bedding and fractures with variable strike that dip moderately to steeply. Two to three flow zones were detected in each borehole. The flow zones consist of bedding-parallel or steeply dipping fractures or a combination of bedding-parallel fractures and moderately to steeply dipping fractures. About 75 to more than 90 percent of the measured flow under pumped conditions was produced by only one of the flow zones in each borehole.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.022 |
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".