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Record W4403091357 · doi:10.1039/d4dd00248b

Leveraging GPT-4 to transform chemistry from paper to practice

2024· article· en· W4403091357 on OpenAlexafffund
Wenyu Zhang, Mason Guy, Jerrica Yang, Lucy Hao, Junliang Liu, Joel M. Hawkins, Sébastien Monfette, Jason E. Hein

Bibliographic record

VenueDigital Discovery · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsStructural Genomics ConsortiumUniversity of TorontoUniversity of British Columbia
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for Innovation
KeywordsChemistryComputer scienceData scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

We present a two-step prompting approach to streamline literature reproduction, transforming published methods into detailed protocols and then into executable experimental steps for the Mettler Toledo EasyMax automated lab reactor.

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)0.022
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.978
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.132
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0090.007
Open science0.0030.010
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0270.041

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 head0.054
GPT teacher head0.357
Teacher spread0.303 · 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 designNot applicable
DomainMethods
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

Citations13
Published2024
Admission routes2
Has abstractyes

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