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Record W2319675781 · doi:10.1021/ac200516c

Compound-Specific Chlorine Isotope Analysis: A Comparison of Gas Chromatography/Isotope Ratio Mass Spectrometry and Gas Chromatography/Quadrupole Mass Spectrometry Methods in an Interlaboratory Study

2011· article· en· W2319675781 on OpenAlexaff
Anat Bernstein, Orfan Shouakar‐Stash, Karin Ebert, Christine Laskov, Daniel Hunkeler, Simon Jeannottat, Kaori Sakaguchi‐Söder, Jens Laaks, Maik A. Jochmann, Stefan Cretnik, Johannes Jäger, Stefan B. Haderlein, Torsten C. Schmidt, Ramón Aravena, Martin Elsner

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryMass spectrometryGas chromatographyIsotope-ratio mass spectrometryChromatographyGas chromatography–mass spectrometryAnalytical Chemistry (journal)ChlorineIsotopes of chlorineOrganic chemistry

Abstract

fetched live from OpenAlex

Chlorine isotope analysis of chlorinated hydrocarbons like trichloroethylene (TCE) is of emerging demand because these species are important environmental pollutants. Continuous flow analysis of noncombusted TCE molecules, either by gas chromatography/isotope ratio mass spectrometry (GC/IRMS) or by GC/quadrupole mass spectrometry (GC/qMS), was recently brought forward as innovative analytical solution. Despite early implementations, a benchmark for routine applications has been missing. This study systematically compared the performance of GC/qMS versus GC/IRMS in six laboratories involving eight different instruments (GC/IRMS, Isoprime and Thermo MAT-253; GC/qMS, Agilent 5973N, two Agilent 5975C, two Thermo DSQII, and one Thermo DSQI). Calibrations of (37)Cl/(35)Cl instrument data against the international SMOC scale (Standard Mean Ocean Chloride) deviated between instruments and over time. Therefore, at least two calibration standards are required to obtain true differences between samples. Amount dependency of δ(37)Cl was pronounced for some instruments, but could be eliminated by corrections, or by adjusting amplitudes of standards and samples. Precision decreased in the order GC/IRMS (1σ ≈ 0.1‰), to GC/qMS (1σ ≈ 0.2-0.5‰ for Agilent GC/qMS and 1σ ≈ 0.2-0.9‰ for Thermo GC/qMS). Nonetheless, δ(37)Cl values between laboratories showed good agreement when the same external standards were used. These results lend confidence to the methods and may serve as a benchmark for future applications.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.007
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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 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

Citations108
Published2011
Admission routes1
Has abstractyes

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