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Record W2026783386 · doi:10.1139/t07-009

Sorption and diffusion of volatile organic compounds through two thermally treated drill mud wastes

2007· article· en· W2026783386 on OpenAlexfundvenueno aff
Marie-Pierre Carignan, Craig B. Lake

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSorptionLeachateWaste managementEnvironmental scienceMunicipal solid wasteSorbentEnvironmental engineeringAdsorptionChemistryEngineering

Abstract

fetched live from OpenAlex

Onshore thermal treatment of offshore drilling mud waste generates a mineral by-product, referred to in this paper as thermally treated drill mud waste (TTDMW). Environmental regulations can result in the generation of significant quantities of this material, and hence it would be beneficial from a sustainability perspective to identify a value-added recycling use for this material. Recycling TTDMW into a primary compacted soil barrier system for municipal solid waste landfills represents one such possible reuse. It is hypothesized that residual amounts of organoclays and organic carbon in the TTDMWs will act as a beneficial sorbent to low levels of volatile organic compounds (VOCs) often found in municipal solid waste leachate. To assess this hypothesis, two different TTDMW materials (from Nova Scotia and the United Kingdom) were subjected to batch and diffusion testing to assess VOC sorption. It is shown that partitioning coefficient (K d ) values obtained from diffusion testing were generally lower than those obtained from batch testing. Contaminant migration modelling of a hypothetical TTDMW barrier system using measured VOC sorption levels is presented in the paper to provide some relevance to the results obtained.Key words: drill mud waste, sorption, VOC, diffusion, landfill liner.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.999

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)0.0020.000

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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 designObservational
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

Citations6
Published2007
Admission routes2
Has abstractyes

Explore more

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