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Record W1969311201 · doi:10.1021/es001930l

Sequential Anaerobic−Aerobic Treatment of Soil Contaminated with Weathered Aroclor 1260

2001· article· en· W1969311201 on OpenAlexaff
Emma R. Master, Vivian Lai, Bianca Kuipers, Walter Cullen, William W. Mohn

Bibliographic record

VenueEnvironmental Science & Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnaerobic exerciseReductive dechlorinationEnvironmental chemistryChemistrySoil contaminationBiodegradationContaminationBioaugmentationSoil waterBioremediationEnvironmental scienceBiologyEcologySoil scienceOrganic chemistryPhysiology

Abstract

fetched live from OpenAlex

Soil contaminated with weathered Aroclor 1260 was bioremediated by sequential anaerobic and aerobic laboratory-scale treatment. The initial concentration was 59 microg of PCBs/g of soil. Following 4 months of anaerobic treatment with an enrichment culture, all of the major components in Aroclor 1260 were completely or partially transformed to less chlorinated PCB congeners. The major products of reductive dechlorination were 24-24-tetrachlorobiphenyl and 24-26-tetrachlorobiphenyl, and the average chlorine substituents per PCB molecule decreased from 6.4 to 5.2. The molar concentration of PCBs did not decrease during the anaerobic treatment. All of the major products formed during the anaerobic treatmentwere degraded in the subsequent aerobic treatment using Burkholderia sp. strain LB400. After 28 days of the aerobic treatment, the concentration of PCBs was reduced to 20 ug/g of soil. PCBs were not significantly removed in aerobic treatments unless they were bioaugmented with LB400. Also, PCB degradation was not detected in soil bioaugmented with LB400 without prior anaerobic treatment. These results confirm the potential for extensive biological destruction of highly chlorinated, weathered PCB congeners in soil.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
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
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations105
Published2001
Admission routes1
Has abstractyes

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