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Record W573555662 · doi:10.1520/gtj20130197

Experimental and Theoretical Modeling of Expansion in Pyritic Shale

2015· article· en· W573555662 on OpenAlexaboutno aff
Shad E. Hoover, Whitney Greenawalt, Brian Tittmann

Bibliographic record

VenueGeotechnical Testing Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringOil shaleGeologyPetroleum engineeringGeomechanics

Abstract

fetched live from OpenAlex

Abstract Expansive pyritic shales are found in black carbonaceous shales throughout the United States as well as in other countries, including Ireland, England, Norway, Canada, and Sweden. Expansion occurs when the pyrite, which occurs either as finely disseminated syngenetic framboids, macroscopic crystals, or diagenetic replacement fossils, oxidizes to form sulfuric acid. Various hydrous sulfates could precipitate in the complex geochemical environment; however, gypsum typically precipitates as the sulfuric acid reacts with the calcareous (calcium carbonate) component of the shale. This paper explores kinetic and passive attempts at measuring the expansion of the shale and introduces a hybrid experimental testing procedure that uses hydrogen peroxide to initiate the expansion process. The normalized expansion (h/H) for the non-intact shale and intact shale core were 0.0008 and 0.0033, respectively, after 84 days. Expansion rates of 3.5 mm/year/m and 1.43 mm/year/m were calculated for the non-intact shale and intact shale core samples, respectively. A theoretical expansion model is developed that uses stoichiometric calculations to determine gypsum volume and discontinuity infilling theory to determine maximum total expansion. Input variables include shale type (intact bedrock, poorly-graded fragments, well-graded fragments), % pyritic shale (%S2), height of the expansion zone, and surcharge pressure. The theoretical model is used to predict maximum height of expansion and time to maximum expansion for the experiments studied and developed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.038
GPT teacher head0.280
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2015
Admission routes1
Has abstractyes

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