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Record W4394937794 · doi:10.1039/d4re90015d

Correction: Applying quantum mechanics to deconvolute benchtop <sup>1</sup>H NMR reaction data

2024· article· en· W4394937794 on OpenAlexafffund
Jiayu Zhang, Tristan Maschmeyer, Ben Shapiro, Sunil Babu Paudel, Matthew C. Leclerc, Jason E. Hein

Bibliographic record

VenueReaction Chemistry & Engineering · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsStructural Genomics ConsortiumUniversity of TorontoUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaRoyal Society of ChemistryCanada Foundation for Innovation
KeywordsChemistryPhysicsComputational chemistry

Abstract

fetched live from OpenAlex

Correction for ‘Applying quantum mechanics to deconvolute benchtop 1 H NMR reaction data’ by Jiayu Zhang et al. , React. Chem. Eng. , 2024, https://doi.org/10.1039/D3RE00583F.

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)0.008
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.199
Threshold uncertainty score0.667

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.132
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.006
Science and technology studies0.0060.005
Scholarly communication0.0060.005
Open science0.0060.006
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.1990.116

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes2
Has abstractyes

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