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Record W2604407638 · doi:10.1021/acs.analchem.6b05168

Development of an Improved Standard Reference Material for Vitamin D Metabolites in Human Serum

2017· article· en· W2604407638 on OpenAlexaff
Karen W. Phinney, Susan S.-C. Tai, Mary Bedner, Johanna E Camara, Rosalind R. C. Chia, Lane C. Sander, Katherine E. Sharpless, Stephen A. Wise, James H. Yen, Rosemary L. Schleicher, Madhulika Chaudhary‐Webb, Khin L Maw, Yasamin Rahmani, Joseph M. Betz, Joyce Merkel, Christopher T. Sempos, Paul M. Coates, Ramón Durazo-Arvizú, Kurtis Sarafin, Stephen P.J. Brooks

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsHealth Canada
Fundersnot available
KeywordsChemistryVitaminChromatographyEnvironmental chemistryFood scienceBiochemistry

Abstract

fetched live from OpenAlex

The National Institute of Standards and Technology (NIST) has developed Standard Reference Material (SRM) 972a Vitamin D Metabolites in Frozen Human Serum as a replacement for SRM 972, which is no longer available. SRM 972a was developed in collaboration with the National Institutes of Health’s Office of Dietary Supplements. In contrast to the previous reference material, three of the four levels of SRM 972a are composed of unmodified human serum. This SRM has certified and reference values for the following 25-hydroxyvitamin D [25(OH)D] species: 25(OH)D 2, 25(OH)D 3, and 3- epi -25(OH)D 3 . The value assignment and certification process included three isotope-dilution mass spectrometry approaches, with measurements performed at NIST and at the Centers for Disease Control and Prevention (CDC). The value assignment methods employed have been modified from those utilized for the previous SRM, and all three approaches now incorporate chromatographic resolution of the stereoisomers, 25(OH)D 3 and 3- epi -25(OH)D 3 .

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)0.022
metaresearch head score (Gemma)0.017
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.004

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 head0.046
GPT teacher head0.378
Teacher spread0.331 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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