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Record W4398334134 · doi:10.7910/dvn/bogpjl

Antibiotics and Weight Outcomes, Diabetes and Microbiome Literature Search

2020· dataset· en· W4398334134 on OpenAlexaff
Archita Srivastava, Bradley C. Johnston

Bibliographic record

VenueHarvard Dataverse · 2020
Typedataset
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsAntibioticsDiabetes mellitusMicrobiomeMedicineBiologyBioinformaticsMicrobiologyEndocrinology

Abstract

fetched live from OpenAlex

Electronic Search Strategy for Association of antibiotics and weight, diabetes and microbiome for MEDLINE

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)0.033
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.122
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.033
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.006
Bibliometrics0.0350.044
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1220.013

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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