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Record W2091333680 · doi:10.1080/09593330902753545

Comparison of one‐ and three‐dimensional soil vapour extraction experiments

2009· article· en· W2091333680 on OpenAlexaff
A. K. Duggal, Richard G. Zytner

Bibliographic record

VenueEnvironmental Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSoil vapor extractionMass transferEnvironmental remediationColumn (typography)Extraction (chemistry)Flow (mathematics)Soil waterChemistryVolumetric flow rateMechanicsQualitative analysisContaminationAnalytical Chemistry (journal)Environmental scienceSoil scienceChromatographyMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Soil vapour extraction (SVE) is a common remediation technology used to clean soil contaminated with gasoline. Even though many studies have been completed on SVE, the majority of them have been at the one-dimensional level, while SVE occurs at a three dimensional level. Accordingly, one-dimensional and radial column laboratory experiments were completed to determine if the experimental configuration made a difference with the results. Two soil types were tested at a variety of flow rates. The contaminant used was toluene. The results were analysed both qualitatively and quantitatively. Analysis of both systems showed them to provide good mass closures. On a qualitative basis, the one-dimensional experiments showed that an increase in flow rates did not result in significant mass transfer limitations for the air flow rates tested. The radial columns revealed mass transfer limitations that were not seen in the one-dimensional column. Quantitative comparison through back-calculated mass transfer coefficients confirmed the trends seen in the qualitative analysis. It is unclear if this is a result of geometry of the radial column or the higher velocities within the radial column. The results indicate that further work with the radial column is necessary to better understand field SVE systems, making it possible to better predict field performance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.571

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)0.0010.000

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 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

Citations7
Published2009
Admission routes1
Has abstractyes

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