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Record W1981505181 · doi:10.1177/0734242x07073652

Assessment of two thermally treated drill mud wastes for landfill containment applications

2007· article· en· W1981505181 on OpenAlexaff
Marie-Pierre Carignan, Craig B. Lake, Todd Menzies

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2007
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWaste managementDrillEnvironmental scienceClay mineralsHydraulic conductivityEnvironmental remediationDrillingLeaching (pedology)Drilling fluidContaminationGeologyMaterials scienceMineralogyMetallurgySoil scienceSoil waterEngineering

Abstract

fetched live from OpenAlex

Offshore oil and gas drilling operations generate significant amounts of drill mud waste, some of which is transported onshore for subsequent thermal treatment (i.e. via thermal remediation). This treatment process results in a mineral waste by-product (referred to as thermally treated drill mud waste; TTDMW). Bentonites are originally present in many of the drill mud products and it is hypothesized that TTDMW can be utilized in landfill containment applications (i.e. cover or base liner). The objective of this paper is to examine the feasibility of this application by performing various physical and chemical tests on two TTDMW samples. It is shown that the two TTDMW samples contained relatively small amounts of clay-sized minerals although hydraulic conductivity values are found to be less than 10(-8) m/s. Organic carbon contents of the samples were approximately 2%. Mineralogy characterization of the samples confirmed varying amounts of smectite, however, peak friction angles for a TTDMW sample was greater than 36 degrees. Chemical characterization of the TTDMW samples show potential leaching of barium and small amounts of other heavy metals. Discussion is provided in the paper on suggestions to assist in overcoming regulatory issues associated with utilization of TTDMW in landfill containment applications.

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)0.006
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 score0.972
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.314
Teacher spread0.295 · 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

Citations17
Published2007
Admission routes1
Has abstractyes

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