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Record W2604070954 · doi:10.1061/9780784480472.027

Unique Approaches of Coal Ash Management Using Geotextile Tubes

2017· article· en· W2604070954 on OpenAlexaff
Christopher G. Timpson

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsTerrafix Geosynthetics (Canada)
Fundersnot available
KeywordsGeotextileWaste managementFly ashCoalDewateringBottom ashEngineeringEnvironmental sciencePower stationGeotechnical engineering

Abstract

fetched live from OpenAlex

Recently the Environmental Protection Agency established national regulations for the safe disposal of coal combustion residuals (CCRs) from coal-fired power plants. These CCR byproducts, such as fly ash and bottom ash, can be a real challenge for both small and large facilities to remove and manage. The Environmental Protection Agency’s comprehensive set of requirements addresses the risks from coal ash disposal like leaking of contaminants into ground water, blowing of contaminants into the air as dust, and the catastrophic failure of coal ash surface impoundments. Geotextile tube dewatering technology can provide a unique approach for the management of coal combustion residuals. By incorporating geotextile tube technology, coal-fired facilities can dewater and contain CCRs. This technology can be utilized in the operation of existing surface impoundments, efforts to beneficially use consolidated ash, and the continued operation of coal-fired operations by direct sluicing the daily flows from the plant into geotextile tubes. This paper will discuss several unique approaches involving the remediation or clean closure of surface impoundments, beneficial use of dewatered CCRs, and direct sluicing operations incorporating geotextile tube technology for the safe disposal of by-products from coal-fired power plants.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.926

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 science0.0010.000
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 head0.248
Teacher spread0.210 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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