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Record W2568085698 · doi:10.5740/jaoacint.smpr2016.014

AOAC SMPR 2016.014:Standard Method Performance Requirements (SMPRs) for Identification and Quantitation of Non-Animal-Derived Proteins in Dietary Supplements

2017· article· en· W2568085698 on OpenAlexaff
Spencer Carter, Joseph M. Betz, Paula N. Brown, Jeff DelFavero, Steven Dentali, Alec Heersink, Jason Hendrickson, Martha Jennens, Vineet Jindal, Suvash Kafley, Rachel Kreider, Adam J. Kuszak, John Lawry, Wenjie Li, Kateřina Maštovská, Elizabeth Mudge, Maria Ofitserova, Punam Patel, Melissa Phillips, Curtis S. Phinney, Catherine A. Rimmer, Brian T Schaneberg, Aniko M Sólyom, Darryl Sullivan, James L Sullivan, Barry Tulk, Robert Wildman, John Williams, Jason Lynn Wubben, Jinchuan Yang, Kurt Young, Joseph Zhou, Garrett Zielinski, Scott G Coates

Bibliographic record

VenueJournal of AOAC International · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsIdentification (biology)ChemistryChromatographyBiotechnologyBiologyBotany

Abstract

fetched live from OpenAlex

Spencer Carter, Joseph M Betz, Paula N Brown, Jeff DelFavero, Steven J Dentali, Alec Heersink, Jason Hendrickson, Martha Jennens, Vineet Jindal, Suvash Kaf

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)0.080
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.080
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.098
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0140.005
Science and technology studies0.0060.003
Scholarly communication0.0050.005
Open science0.0100.006
Research integrity0.0140.005
Insufficient payload (model declined to judge)0.0130.034

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 head0.030
GPT teacher head0.370
Teacher spread0.340 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2017
Admission routes1
Has abstractyes

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