Using helicopter electromagnetic (HEM) surveys to identify potential hazards at coal-waste impoundments: Examples from West Virginia
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Bibliographic record
Abstract
Abstract We have used 14 multifrequency helicopter-borne electromagnetic (HEM) surveys to determine the internal structure and integrity of mine-impoundment structures in West Virginia, U.S.A. — the first time such technology has been applied in this way and apparently well suited for such activities. The HEM surveys identified areas of concern in each of the impoundments investigated. In most cases, these were areas where filtrate was emerging high on the downstream embankment and represented an erosion risk. Of greater concern, the HEM survey identified thick bodies of slurry that remained unconsolidated and were buried deep beneath the embankment's crest of some impoundments. Ground confirmation activities indicated that HEM survey interpretations provided an accurate representation of the conductivity distribution within coal-waste impoundments. We then interpreted the conductivity/depth images from the HEMsurveys to provide a snapshot of hydrologic conditions that existed within the impoundment at the time of the survey. Resistivity profiles were obtained at the inactive impoundment along segments of flight lines from the HEM survey. HEM and resistivity surveys detected a conductive layer at a depth of about 7 m that was interpreted to be unconsolidated coal slurry. The methods also detected conductive bodies at a depth of about 26 m that were interpreted to be flooded mine works. Resistivity surveys from these segments corroborated HEM data, thereby providing independent confirmation of the HEM data and its processing. The resistivity and HEM surveys indicated a resistive surface layer where the coarse coal refuse was placed. Beneath the resistive surface layer is a conductive layer of unconsolidated or partially consolidated coal slurry. These highly loaded bodies of unconsolidated slurry are susceptible to solifluction, which can threaten embankment stability. Underground mine workings were identified in the HEM data from one impoundment.
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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 | 0.001 |
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 it