MétaCan
Menu
Back to cohort
Record W2039242302 · doi:10.1021/ac0484530

Determination of Ephedrine Alkaloids in Dietary Supplement Standard Reference Materials

2005· article· en· W2039242302 on OpenAlexaff
Lane C. Sander, Katherine E. Sharpless, Mary B. Satterfield, Toshihide Ihara, Karen W. Phinney, James H. Yen, Stephen A. Wise, Martha L. Gay, Joseph Lam, Margaret McCooeye, Graeme J. Gardner, Catharine Fraser, Ralph E. Sturgeon, Mark Roman

Bibliographic record

VenueAnalytical Chemistry · 2005
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsNational Research Council Canada
FundersNational Institutes of HealthOffice of Dietary Supplements
KeywordsChemistryNISTDietary supplementCertified reference materialsSample preparationChromatographyEphedrineMatrix (chemical analysis)Food sciencePharmacologyDetection limitComputer science

Abstract

fetched live from OpenAlex

A suite of five ephedra-containing dietary supplement Standard Reference Materials (SRMs) has been issued by the National Institute of Standards and Technology (NIST) with certified values for ephedrine alkaloids, synephrine, caffeine, and selected toxic trace elements. The materials represent a variety of natural, extracted, and processed sample matrixes that provide different analytical challenges. The constituents have been determined by multiple independent methods with measurements performed by NIST and by three collaborating laboratories. The methods utilized different sample extraction and cleanup steps in addition to different instrumental analytical techniques and approaches to quantification. In addition, food-matrix proximates were determined by National Food Processor Association laboratories for one of the ephedra-containing SRMs. The SRMs are primarily intended for method validation and for use as control materials to support the analysis of dietary supplements and related botanical materials.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.027
GPT teacher head0.343
Teacher spread0.317 · 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.

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

Citations53
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicPharmacology and Obesity TreatmentFrench-language works237,207