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Record W2139401010 · doi:10.1039/b302594m

Direct filter vaporization with induction heating-electrothermal vaporization (IH-ETV) and a foam carbon surface

2003· article· en· W2139401010 on OpenAlexaff
Eric D. Salin, J. M. Ren

Bibliographic record

VenueJournal of Analytical Atomic Spectrometry · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsMcGill University
Fundersnot available
KeywordsVaporizationMaterials scienceCarbon fibersFilter (signal processing)Analytical Chemistry (journal)ChemistryComposite materialChromatographyOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

An inductively heated electrothermal vaporizer (IH-ETV) sample introduction system was tested using a foam carbon surface with and without a standard air sampling filter employing ICP-AES excitation with a semiconductor based commercial detection system. Liquid detection limits using 10 µl injections were roughly comparable to those obtained using conventional nebulization on the same instrument. Mass detection limits ranged from 0.5 ng (Zn at 213.9 nm with a foam disk) to 20 ng (Pb at 220.4 nm with a filter). This performance suggests that IH-ETV vaporization of filters might find applicability in environmental air sampling studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2003
Admission routes1
Has abstractyes

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