Far-Field Ground Strain Failure Mode Assessment for Mineral Extraction Near Dams
Bibliographic record
Abstract
Steeply sloped, high relief landforms with fractured sedimentary geology in proximity to underground mine voids have the potential to produce far-field ground strains which initiate strain failure modes for cross valley dams. The Ryerson Station Dam breach in 2005 brought attention to the possibility of increased hazard to dams due to far-field ground strain phenomena from underground mining.;The mapping study found that three United States Army Corps of Engineers (U.S.ACE) owned and operated dams were within the 1-mile buffer radius, and four within the 2.7-mile buffer radius of mine permits in West Virginia. Some U.S.ACE dams can be very large in their length, changing the offset distances used herein significantly. There were zero U.S.ACE dams found to have any underground mine permit directly underneath their location. The total frequency of occurrence for U.S.ACE dams considered nearby to underground mine permits in WV was 17.4%.;There are many more dams considered in the National Inventory of Dams (NID) than U.S.ACE dams alone. The same buffer radii were chosen to assess the offset distances of NID dams to underground mine permits as were used for the U.S.ACE dam assessment. There were found to be 115 NID dams within the 2.7-mile buffer radius with 79 of those within 1 mile, and 45 directly undermined. The total frequency of occurrence for NID dams existing nearby to permitted underground mines was found to be 18.9%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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 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".