Cone-based geophysical imaging: A proposed solution to a challenging problem
Bibliographic record
Abstract
There are many locations throughout the world where subsurface contamination impacts the natural environment, with potentially serious consequences for the quality of our water and for human heath. At U.S. Department of Energy (DOE) sites alone there are estimated to be “about 6.4 billion cubic meters of contaminated soil, groundwater and other environmental media” (DOE Environmental Management Science Program announcement 02–03). One of the initial steps in dealing with a contaminated site is that referred to as site characterization. During site characterization, measurements are made that allow for the development of an accurate model of the physical, chemical, biological, and hydrogeological properties of the subsurface. Such a model is required to design an appropriate plan for remediation of a contaminated site and can also be used, and continually updated, for short-term or long-term monitoring of the site. Site characterization can involve locating and identifying a known or suspected contaminant, and can also involve determining the properties of the subsurface controlling the fate and transport of the contaminant. The challenging problem we face, at many sites, is identifying an approach to site characterization that provides the required information about the subsurface while minimizing the risks associated with contacting the contaminated region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".