Unique applications of MMR to track preferential groundwater flow paths in dams, mines, environmental sites, and leach fields
Bibliographic record
Abstract
Groundwater systems have been notoriously difficult to map with high degrees of accuracy. As a result, not only have traditional geophysical methods proven inaccurate for groundwater characterization work, but they are often costly in terms of time, money, and environmental trauma. This paper describes a unique application of magnetometric resistivity or MMR (Edwards and Nabighian, 1991) for groundwater mapping and modeling, which is high-speed, accurate, minimally invasive, and cost effective. This method has now been deployed at many different sites all over the United States and in other countries like Canada, England, Peru, Sri Lanka, and Argentina. In 2007, the method was employed at 17 dams; some are large well-known structures in the United States. Through two case histories, this paper will assess the effectiveness of this methodology.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".