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Record W2507212591 · doi:10.1080/10889868.2017.1312265

Large-scale bioventing degradation rates of petroleum hydrocarbons and determination of scale-up factors

2017· article· en· W2507212591 on OpenAlexaff
Michael J. Mosco, Richard G. Zytner

Bibliographic record

VenueBioremediation Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnvironmental scienceBioremediationEnvironmental remediationGroundwater remediationPetroleumSoil waterMoistureDegradation (telecommunications)Environmental chemistryGasolineEnvironmental engineeringSoil scienceContaminationWaste managementChemistryEcologyEngineering

Abstract

fetched live from OpenAlex

Bioventing is a cutting edge, nondestructive treatment method that uses indigenous soil microorganisms in situ to remediate petroleum hydrocarbons in the unsaturated soil zone. Transferring the application of this technology to a field environment still has some uncertainties due to scale-up challenges. In order to identify the scale-up factor, a 80-kg soil reactor system was developed, consisting of a custom-made reactor, climate chamber, low-flow venting system, and an off-gas capture device. Sandy and clayey soils were tested with known concentrations of spiked synthetic gasoline. Various environmental conditions were monitored, which included moisture levels, pH, microbial levels, and nutrient and oxygen levels. Results show a second-stage degradation rate similar to the degradation rate obtained from research conducted with a 4-kg reactor, giving an average scale-up factor of 2.3 ± 0.4. The completed research shows that working with a 80-kg laboratory reactor is feasible, yet not always necessary for the development of scale-up factors. A complimentary study with aged soil contaminants was performed and yielded degradation rates that were significantly reduced.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.366

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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