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Record W1971908963 · doi:10.1366/0003702011952235

Prediction of Multiple Matrix Interferences in Inductively Coupled Plasma Mass Spectrometry

2001· article· en· W1971908963 on OpenAlexaff
John W. Tromp, Amanda Cole, Hai Ying, Eric D. Salin

Bibliographic record

VenueApplied Spectroscopy · 2001
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsMcGill University
Fundersnot available
KeywordsCalibrationInterference (communication)StandardizationMatrix (chemical analysis)Basis (linear algebra)Mass spectrometryChemistrySimple (philosophy)Inductively coupled plasma mass spectrometryAnalytical Chemistry (journal)Biological systemAlgorithmComputer scienceChromatographyStatisticsMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Matrix effects for pairs of interferents (Al, Na, K, Ba, and Cs) were investigated and compared to predictions of the amount of interference determined by single interferent experiments in order to test a model called the total interference level (TIL), which assumes that the effects of different interferents add linearly. The TIL model is part of an Autonomous Instrument and is designed to indicate when a simple default calibration, such as external calibration or internal standardization, is inadequate for the desired accuracy of analysis. The performance of the TIL model was examined in terms of a daily calibration basis, which should be more accurate, and an occasional calibration basis, which is more convenient, considering simple external standardization and internal standardization as the techniques to be tested for desired accuracy. The results are encouraging for multiple interferences and show that the TIL model can serve a useful function in predicting calibration errors, even given the presence of instrument drift on ICP-MS.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.060
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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 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

Citations6
Published2001
Admission routes1
Has abstractyes

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