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Record W2073596624 · doi:10.1021/es0349002

Chiral PCB Signatures in Air and Soil:  Implications for Atmospheric Source Apportionment

2004· article· en· W2073596624 on OpenAlexaboutno aff
Matthew Robson, Stuart Harrad

Bibliographic record

VenueEnvironmental Science & Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsTopsoilVolatilisationEnantioselective synthesisEnvironmental chemistryEnvironmental scienceEnantiomerVegetation (pathology)ChemistrySoil waterDegradation (telecommunications)Soil scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Enantiomeric fractions (EFs) of chiral PCBs 95, 136, and 149 were measured in samples of topsoil and outdoor air at one urban and one rural location in the U.K. West Midlands between early 2001 and early 2002. While EFs in air were essentially racemic, those in topsoil indicated appreciable enantioenrichment of the second eluting enantiomer for PCB 95 and the (+) enantiomer for PCBs 136 and 149. This suggests (i) that essentially all atmospheric PCBs at both sites arise from racemic (i.e, primary) sources, rather than volatilization from soil and (ii) that appreciable enantioselective degradation of the monitored PCBs in topsoil occurs. This is one of only two reports of enantioselective degradation of PCBs in soil worldwide and is particularly noteworthy as it is occurring at PCB concentrations (e.g., 5.9 pg g(-1) for PCB 136) that are typical of the U.K. and other industrialized countries. The extent of enantioselective degradation in this study for PCBs 95 and 136 is consistent with those reported for soils in the Greater Toronto area (GTA). In contrast, enantioselective degradation of PCB 149 observed in this study is--while consistent with that reported for U.K. lacustrine sediments--in excess of that observed in either the GTA soil study or in U.S. lake sediments.

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 score0.757
Threshold uncertainty score0.956

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 studies0.0000.003
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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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

Citations95
Published2004
Admission routes1
Has abstractyes

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