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Record W2065548555 · doi:10.1002/mas.20257

Environmental analysis by inductively coupled plasma mass spectrometry

2009· review· en· W2065548555 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMass Spectrometry Reviews · 2009
Typereview
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMass spectrometryEnvironmental analysisAnalyteInductively coupled plasma mass spectrometrySample preparationDetection limitCertified reference materialsAnalytical Chemistry (journal)Inductively coupled plasmaMatrix (chemical analysis)CalibrationSample (material)Environmental chemistryChromatographyPlasma

Abstract

fetched live from OpenAlex

This article reviews the numerous ways in which inductively coupled plasma mass spectrometry has been used for the analysis of environmental samples since it was commercially introduced in 1983. Its multielemental isotopic capability, high sensitivity and wide linear dynamic range makes it ideally suited for environmental analysis. Provided that some care is taken during sample preparation and that appropriate calibration strategies are used to circumvent non-spectroscopic interferences, the technique is readily applicable to the analysis of a wide variety of environmental samples (natural waters, soils, rocks, sediments, vegetation, etc.), using quadrupole, time-of-flight or double-focusing sector-field mass spectrometers. In cases where spectroscopic interferences arising from the sample matrix cannot be resolved, then separation methods can be implemented either on- or off-line, which can simultaneously allow analyte preconcentration, thus further decreasing the already low detection limits that are achievable. In most cases, the blank, prepared by following the same steps as for the sample but without the sample, limits the ultimate detection limits that can be reached.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0320.002

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 head0.045
GPT teacher head0.318
Teacher spread0.274 · 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