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Record W2818953484 · doi:10.5740/jaoacint.smpr2018.005

Standard Method Performance Requirements (SMPRs®) 2018.005: Determination of Kavalactones and/or Flavokavains from Kava (Piper methysticum)

2018· article· en· W2818953484 on OpenAlexaff
Steven Dentali, Cristina Amarillas, T. O. Blythe, Paula N. Brown, Anton Bzhelyansky, Christine Fields, Holly E. Johnson, Scott Krepich, Adam J. Kuszak, Charles Metcalfe, Marı́a Monagas, Elizabeth Mudge, Salvatore Parisi, K. Reif, Catherine A. Rimmer, Myron Sasser, Aniko M Sólyom, Jeremy Stewart, John Szpylka, Michael Tims, Richard B. van Breemen, Hong You, Hui Zhao, Garrett Zielinski, Scott G Coates

Bibliographic record

VenueJournal of AOAC International · 2018
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plant Extracts Effects
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsKavaChromatographyPiperaceaeChemistryPiperTraditional medicineMedicine

Abstract

fetched live from OpenAlex

Steven J Dentali, Cristina Amarillas, Tyler Blythe, Paula N Brown, Anton Bzhelyansky, Christine Fields, Holly E Johnson, Scott Krepich, Adam Kuszak, Charle

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)0.050
metaresearch head score (Gemma)0.081
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: Methods · Consensus signal: Methods
Teacher disagreement score0.050
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.081
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0120.004
Science and technology studies0.0060.005
Scholarly communication0.0050.005
Open science0.0080.005
Research integrity0.0130.005
Insufficient payload (model declined to judge)0.0240.037

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 head0.038
GPT teacher head0.374
Teacher spread0.336 · 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
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

Citations10
Published2018
Admission routes1
Has abstractyes

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