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Record W4386400255 · doi:10.1016/j.gastha.2023.08.016

High Variability on Alcohol Intake Threshold in Articles Using the MAFLD Acronym

2023· article· en· W4386400255 on OpenAlexaff
María Hernández‐Tejero, Samhita Ravi, Jaideep Behari, Gavin E. Arteel, Juan Pablo Arab, Ramón Bataller

Bibliographic record

VenueGastro Hep Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthAllerganPfizerFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaGenentechFondo Nacional de Desarrollo Científico y TecnológicoCelgeneNational Institute of Diabetes and Digestive and Kidney DiseasesGilead Sciences
KeywordsAcronymAlcoholAlcohol intakePsychologyChemistryPhilosophyLinguisticsOrganic chemistry

Abstract

fetched live from OpenAlex

discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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)0.005
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.041
GPT teacher head0.321
Teacher spread0.281 · 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.

Study designObservational
DomainReporting
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

Citations1
Published2023
Admission routes1
Has abstractno

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