Selected borehole geophysical logs from three contaminant sites in California, Wisconsin, and New Jersey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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 it