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Record W2316338206 · doi:10.1021/es1042113

Cloud and Fog Processing Enhanced Gas-to-Particle Partitioning of Trimethylamine

2011· article· en· W2316338206 on OpenAlexafffundabout
Peter Rehbein, Cheol–Heon Jeong, Maygan L. McGuire, Xiaohong Yao, Joel C. Corbin, Greg J. Evans

Bibliographic record

VenueEnvironmental Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrimethylamineRelative humidityAerosolParticle (ecology)Environmental scienceEnvironmental chemistryHumidityMass spectrometryGas phaseAtmospheric sciencesChemistryMeteorologyGeographyChromatographyGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

An aerosol time-of-flight mass spectrometer (ATOFMS) was used to detect trimethylamine (TMA) in 0.52-1.9 μm particles at urban and rural sites in Southern Ontario during the summer and winter of 2007. During the summer, TMA-containing particles were observed exclusively during high relative humidity or fog events at both the urban and rural sites. In the wintertime, greater concentrations of TMA-containing particles were linked to cloud processing of aerosol in air masses originating from over agricultural and livestock areas. A laboratory study revealed that, at high relative humidity (∼ 100%), gas phase TMA at concentrations ranging from 2 to 20,000 ppt partitions preferentially to acidic particles present in the ambient air. On the basis of the field and laboratory studies, it appears that gas phase TMA present in ambient air partitions onto pre-existing particles preferentially during periods of acidic cloud/fog processing, leading to the presence of TMA-containing particles in the 0.52-1.9 μm size range.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.133
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations115
Published2011
Admission routes3
Has abstractyes

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