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Record W2158950963 · doi:10.1002/0471732877.emd116

Flame Atomic Emission Spectrometry and Atomic Absorption Spectrometry

2006· other· en· W2158950963 on OpenAlexaff
Andrew W. Lyon, Martha E. Lyon

Bibliographic record

VenueEncyclopedia of Medical Devices and Instrumentation · 2006
Typeother
Languageen
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAtomic spectroscopyAtomic absorption spectroscopySpectroscopyAnalytical Chemistry (journal)ChemistryEmission spectrumAbsorption spectroscopyMass spectrometryPhotometry (optics)Flame photometryIonizationSpectral lineAtomic emission spectroscopyHollow-cathode lampInductively coupled plasmaOpticsEnvironmental chemistryPlasmaPhysicsIon

Abstract

fetched live from OpenAlex

Abstract Observation in the 1700s that candlelight changed color when different materials were introduced into a flame prompted studies that revealed that elements emit characteristic colors of light that consist of line spectra. Kirchhoff’s laws of Spectroscopy were established in the late 1800s and describe both emission and absorption of light. The observation that atoms of each element can emit and absorb light at specific wavelengths is a fundamental property of matter that rapidly became an analytical tool and contributed to development of structural models of the atom. The theoretical basis and instrument components used for flame emission spectrometry (also called flame photometry) and flame and flameless atomic absorption spectroscopy are described with general comments on sensitivity and susceptibility to chemical, spectral, ionization and matrix interferences. Modern instruments that use flame, electrothermal (or flameless) or inductively coupled plasma designs are flexible, reliable and sensitive and are used in reference laboratories to assess the concentration of trace elements and heavy metals in biological fluids to support medical diagnosis and treatment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.232
Teacher spread0.229 · 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 designNot applicable
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

Citations2
Published2006
Admission routes1
Has abstractyes

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