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Record W2064227505 · doi:10.1139/t00-097

Fracturing in low-permeability soils for remediation of contaminated ground

2001· article· en· W2064227505 on OpenAlexfundvenueno aff
Ron CK Wong, Marolo Alfaro

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTiltmeterGeotechnical engineeringGeologyElectrical resistivity tomographyPermeability (electromagnetism)ExcavationHydraulic fracturingFracture (geology)Environmental remediationElectrical resistivity and conductivityContaminationEngineering

Abstract

fetched live from OpenAlex

This paper presents a field study on hydraulic fracturing for in situ remediation of contaminated ground. Sand-propped hydraulic fractures were placed from vertical and horizontal wells at a test facility. Field excavations were conducted to expose the fractures and inspect their distribution and geometry. Fractures that were mapped by field excavation were found to be near horizontal, implying that the soil formation is overconsolidated. It was also observed that the sand "proppant" was thicker at locations where the soil layers were relatively weak or contained weak fissures. Electrical resistivity tomography (ERT) was also conducted in an attempt to map the fractures. There was no indication that fractures were being mapped by this geophysical technique. Fracture mapping based on tiltmeter data analyses conformed closely with the actual fracture placement in the vertical well but did not properly predict the actual fracture placement in the horizontal well.Key words: hydraulic fracturing, field test, low-permeability soil, electrical resistivity tomography, tiltmeters, horizontal well, vertical well.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations21
Published2001
Admission routes2
Has abstractyes

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