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Record W2084312210 · doi:10.1080/10739140601000616

Construction and Evaluation of a Low Cost Interface for the Determination of Elemental Speciation by Gas Chromatography Coupled to Inductively Coupled Plasma Mass Spectrometry (GC‐ICP‐MS)

2006· article· en· W2084312210 on OpenAlexfundno aff
Michael J. Watts, Alex W. Kim, Daniel S. Vidler, Richard O. Jenkins, John F. Hall, Chris F. Harrington

Bibliographic record

VenueInstrumentation Science & Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilBritish Geological SurveyNational Research Council CanadaGovernment of the United Kingdom
KeywordsChemistryInductively coupled plasma mass spectrometryInjectorChromatographyIsotope dilutionMass spectrometryAnalyteGas chromatographyAnalytical Chemistry (journal)Inductively coupled plasmaArgonGas chromatography–mass spectrometryCertified reference materialsDetection limitPlasma

Abstract

fetched live from OpenAlex

Abstract The construction and evaluation of a low cost, easily demountable interface to couple capillary gas chromatography to inductively coupled plasma mass spectrometry detection is described. Using this interface, the capillary column can be maintained at a high temperature through to the tip of the torch injector using a transfer line heated by a combination of hot argon and electrical resistance. The interface is suitable for analytes with boiling points up to 230°C, allowing for the analysis of low and high boiling analytes in a single injection. The system was evaluated by the determination of the butyltin species in a marine sediment CRM using conventional calibration with tripropyltin dichloride as the internal standard and the measurement of methylmercury in a tuna fish CRM via species‐specific isotope dilution analysis. Detailed information on the design and construction of the interface are included to facilitate its construction and use by other workers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.416

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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 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

Citations8
Published2006
Admission routes1
Has abstractyes

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