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Record W2076021491 · doi:10.1080/10889868.2013.807772

Degradation Rates for Petroleum Hydrocarbons Undergoing Bioventing at the Meso-Scale

2013· article· en· W2076021491 on OpenAlexafffund
Alamgir Akhtar Khan, Richard G. Zytner

Bibliographic record

VenueBioremediation Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLoamEnvironmental remediationSoil waterEnvironmental scienceGroundwater remediationSiltSoil scienceSoil contaminationEnvironmental engineeringDegradation (telecommunications)ChemistryGeologyContaminationEcology

Abstract

fetched live from OpenAlex

Bioventing can be effective for the remediation of soil contaminated with petroleum hydrocarbons. However, implementing laboratory results in field scenarios is difficult due to the lack of scale-up factors. Accordingly, laboratory bioventing experiments were undertaken at the meso-scale and then compared to previously completed micro-scale tests to evaluate the important scale-up factor. The developed meso-scale system holds 4 kg of soil, with bioventing conditions controlled from a nutrient, airflow and water content perspective. Three soils were tested, and categorised as sandy, clayey, and loam soil. Results over a thirty-day period showed a two-stage degradation pattern that encompassed first-order degradation rates as compared to the single-stage first-order degradation rate determined in the micro-scale study. For the first stage (0 to 8 d), the degradation rate for sandy soil was 0.598 d-1, with the clayey soil at 0.460 d-1, and mixed soil at 0.477 d-1. After 8 days, the degradation rate constant for the sandy soil dropped to 0.123 d-1, with the clayey soil dropping to 0.075 d-1, and the degradation rate for the mixed soil dropping to 0.093 d-1. Comparison of the measured degradation rate values with the results from the micro-scale experiments gave scale-up factors varying from 1.9 to 2.7 for the types of soil considered in the current study. These differences in degradation rates between the two scales show the importance of scale-up factors when transferring feasibility study results to the field.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2013
Admission routes2
Has abstractyes

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