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Record W2980830562 · doi:10.33915/etd.6543

Far-Field Ground Strain Failure Mode Assessment for Mineral Extraction Near Dams

2017· dissertation· en· W2980830562 on OpenAlexaboutno aff
Harold Russell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsMileGeologyGeotechnical engineeringMining engineeringRADIUSHydrology (agriculture)Geodesy

Abstract

fetched live from OpenAlex

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%.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.310
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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