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Record W2790286048 · doi:10.1016/j.trac.2018.03.009

Multi-year inter-laboratory exercises for the analysis of illicit drugs and metabolites in wastewater: Development of a quality control system

2018· article· en· W2790286048 on OpenAlexfundno aff
Alexander L.N. van Nuijs, Foon Yin Lai, Frederic Béen, María Jesús Andrés-Costa, Leon Barron, Jose Antonio Baz‐Lomba, Jean-Daniel Berset, Lisa Benaglia, Lubertus Bijlsma, Daniel A. Burgard, Sara Castiglioni, Christophoros Christophoridis, Adrian Covaci, Pim de Voogt, Erik Emke, Despo Fatta‐Kassinos, Jerker Fick, Félix Hernández, Cobus Gerber, Iria González‐Mariño, Roman Grabic, Teemu Gunnar, Kurunthachalam Kannan, Sara Karolak, Barbara Kasprzyk‐Hordern, Zenon J. Kokot, Ivona Krizman-Matasic, Angela Li, Xiqing Li, Arndís Sue Ching Löve, Miren López de Alda, Ann‐Kathrin McCall, Markus R. Meyer, Herbert Oberacher, Jake O’Brien, José Benito Quintana, Malcolm J. Reid, Serge Schneider, Susana Sadler Simões, Νikolaos S. Τhomaidis, Kevin V. Thomas, Viviane Yargeau, Christoph Ort

Bibliographic record

VenueTrAC Trends in Analytical Chemistry · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsNatural Sciences and Engineering Research Council of CanadaSeventh Framework ProgrammeVlaamse regeringXunta de GaliciaGeneralitat ValencianaFonds Wetenschappelijk OnderzoekEuropean Cooperation in Science and TechnologyMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaNational Science FoundationGeneralitat de CatalunyaMinisterstvo Školství, Mládeže a TělovýchovyStavros Niarchos FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsWastewaterAnalyteExternal quality assessmentEnvironmental scienceComputer scienceChromatographyEngineeringEnvironmental engineeringOperations managementChemistry

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.328
Teacher spread0.296 · 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.

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

Citations106
Published2018
Admission routes1
Has abstractno

Explore more

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