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Record W2955626121 · doi:10.1088/1681-7575/ab27e5

Establishment of measurement traceability for peptide and protein quantification through rigorous purity assessment—a review

2019· article· en· W2955626121 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.

Bibliographic record

VenueMetrologia · 2019
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTraceabilityMetrologyComputer scienceConsistency (knowledge bases)CalibrationSystems engineeringRisk analysis (engineering)Medical physicsOperations researchManagement scienceMedicineEngineeringMathematicsStatisticsSoftware engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The health of their populations and efficient health care systems are of critical importance to the economic and social well-being of nations. Accurate and comparable peptide/protein measurements are required in support of diagnosis, prognosis, monitoring and treatment of widespread diseases (e.g. diabetes). The required consistency of measurement results can be achieved by making them traceable to stated references and through the development of Reference Measurement Systems. The review mainly concentrates on the progress made in the Protein Analysis Working Group of the Consultative Committee for Amount of Substance: Metrology in Chemistry and Biology (CCQM-PAWG) in establishing Primary Calibration Reference Services in the emerging area of health markers such as peptides/proteins. Primary Calibration Reference Services are technical capabilities for composition assignment, commonly as the mass fraction content, of pure substances or solutions thereof. It is a core technical competency for National Measurement Institutes (NMIs) . A limited number of key comparisons, foreseen by the CCQM-PAWG strategy, are discussed that enable NMIs providing measurement services in peptide/protein analysis to test and demonstrate their capabilities. In addition, the review examines the development and improvement of analytical methods and metrological models that are required to meet the needs of NMIs and associated clinical stakeholders.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.328
Teacher spread0.286 · 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