Selected borehole geophysical logs from three contaminant sites in California, Wisconsin, and New Jersey
Bibliographic record
Abstract
Borehole geophysical logs were collected to characterize bedrock aquifers at three contamination sites located in California, Wisconsin, and New Jersey. The data were collected by the U.S. Geological Survey (USGS) and the University of Guelph from 2014 to 2015 as part of the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) initiatives to apply geophysical methods at fractured-rock sites contaminated with chlorinated solvents. Logs were collected in open boreholes completed in fractured rock. Each borehole was logged with natural gamma, electromagnetic induction, normal resistivity, single-point resistance, spontaneous potential, induced polarization, magnetic susceptibility, acoustic imaging, and nuclear magnetic resonance methods. In addition, total volatile organic compound (TVOC) samples were extracted from solid core and collected at discrete locations that averaged every 0.5 to 1.0 foot along depth of the borehole. The borehole geophysical data are summarized for each of the sites. These data were used in a machine learning exercise that explored the relations between borehole log measurements and contaminant distribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".