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Record W2831095750 · doi:10.5740/jaoacint.smpr2018.003

Standard Method Performance Requirements (SMPRs®) 2018.003: Quantitation of Milk by ELISA-Based Methods

2018· article· en· W2831095750 on OpenAlexaff
Samuel Benrejeb Godefroy, Jupiter M. Yeung, Gerardo Albornoz, Dave Almy, Ashley Beasley Green, Sneh Bhandari, Renuka Brown, Paulo Da Costa, Valentine Digonnet, Hirotoshi Doi, Aurélie Dubois, Eric A. E. Garber, Tao Geng, Adi Gilboa-Geffen, Phil Goodwin, Sigrid Haas‐Lauterbach, Harvey E. Indyk, Diana C Kavolis, Terry Koerner, Markus Lacorn, Alexandria Lau, Yasutaka Nishiyama, Gavin O’Connor, Roland Poms, Bert Pöpping, Prasad Rallabhandi, Michael Ryan, Girdhari M. Sharma, Masahiro Shoji, Christy Swoboda, Masayoshi Tomiki, Antonietta Wallace, Charles Yang, Jinchuan Yang

Bibliographic record

VenueJournal of AOAC International · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsHealth Canada
Fundersnot available
KeywordsChromatographyChemistry

Abstract

fetched live from OpenAlex

Samuel Godefroy, Jupiter Yeung, Gerardo Albornoz, Dave Almy, Ashley Beasley Green, Sneh Bhandari, Renuka Brown, Paulo Da Costa, Valentine Digonnet, Hirotos

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)0.052
metaresearch head score (Gemma)0.076
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 score0.052
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.076
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.004
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0120.005
Research integrity0.0140.005
Insufficient payload (model declined to judge)0.0270.051

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 head0.058
GPT teacher head0.389
Teacher spread0.332 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

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