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Record W2901110646 · doi:10.1016/j.aca.2018.11.036

Validation and inter-laboratory study of selective hydride generation for fast screening of inorganic arsenic in seafood

2018· article· en· W2901110646 on OpenAlexaff
Karel Marschner, Ásta H. Pétursdóttir, Patrick Bücker, Andrea Raab, Jörg Feldmann, Zoltán Mester, Tomáš Matoušek, Stanislav Musil

Bibliographic record

VenueAnalytica Chimica Acta · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsNational Research Council Canada
FundersH2020 Marie Skłodowska-Curie ActionsÚstav analytické chemie, Akademie Věd České RepublikyHorizon 2020 Framework ProgrammeMinisterstvo Školství, Mládeže a TělovýchovyEuropean Commission
KeywordsChemistryCertified reference materialsHydrideArsineDetection limitArsenicInductively coupled plasma mass spectrometryChromatographyHydrogen peroxideExtraction (chemistry)Nitric acidMass spectrometryEnvironmental chemistryHydrogenInorganic chemistryCatalysisBiochemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.254
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 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

Citations32
Published2018
Admission routes1
Has abstractno

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