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Record W2038444745 · doi:10.1002/fact.1021

Field detection and identification of a bioaerosol suite by pyrolysis‐gas chromatography‐ion mobility spectrometry*

2001· article· en· W2038444745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueField Analytical Chemistry & Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAerosolBioaerosolIon-mobility spectrometryChemistryPyrolysisGas chromatographyChromatographyMass spectrometryEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract Improvements were made to a pyrolysis‐gas chromatography‐ion mobility spectrometry (Py‐GC‐IMS) stand‐alone biodetector to provide more pyrolyzate compound information to the IMS detector module. Air carrier gas flowing continuously through the pyrolysis tube, the rate of air flow, and pyrolysis rate were found to improve the relative quality and quantity of pyrolyzate compounds detected by the IMS detector compared to earlier work. These improvements allowed a greater degree of confidence in the correlation of biological aerosols obtained in outdoor testing scenarios to a standard GC‐IMS biological aerosol dataset. The airflow improvement allowed more biomarker compounds to be observed in the GC‐IMS data domain for aerosols of gram‐negative Erwinia herbicola (EH) and ovalbumin protein as compared to previous studies. Minimal differences were observed for gram‐positive spores of Bacillus subtilis var. globigii (BG) from that of earlier work. Prior outdoor aerosol challenges dealt with the detection of one organism, either EH or BG. Biological aerosols were disseminated in a Western Canadian prairie and the Py‐GC‐IMS was tested for its ability to detect the biological aerosols. The current series of outdoor trials consisted of three different biological aerosol challenges. Forty‐two trials were conducted and a simple area calculation of the GC‐IMS data domain biomarker peaks correlated with the correct bioaerosol challenge in 30 trials (71%). In another 7 trials, the status of an aerosol was determined to be biological in origin. Two additional trials had no discernible, unambiguous GC‐IMS biological response, because they were blank water sprays. Reproducible limits of detection were at a concentration of less than 0.5 bacterial analyte‐containing particle per liter of air. In order to realize this low concentration, an aerosol concentrator was used to concentrate 2000 l of air in 2.2 min. Previous outdoor aerosol trials have shown the Py‐GC‐IMS device to be a credible detector with respect to determining the presence of a biological aerosol. The current series of outdoor trials has provided a platform to show that the Py‐GC‐IMS can provide information more specific than a biological or non‐biological analysis to an aerosol when the time of dissemination is unknown to the operator. The Py‐GC‐IMS is shown to be able to discriminate between aerosols of a gram‐positive spore (BG), a gram‐negative bacterium (EH), and a protein (ovalbumin). © 2001 John Wiley & Sons, Inc. Field Analyt Chem Technol 5: 190–204, 2001

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.015
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.212
Teacher spread0.208 · 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