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Record W2799982373 · doi:10.1002/cmr.a.21415

Evolutionary algorithms and nuclear magnetic resonance of oriented molecules

2016· article· en· W2799982373 on OpenAlexaff
E. Elliott Burnell, C. A. de Lange, Ronald Y. Dong, W. Leo Meerts, Adrian C. J. Weber

Bibliographic record

VenueConcepts in Magnetic Resonance Part A · 2016
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of WinnipegUniversity of British Columbia
Fundersnot available
KeywordsHexamethylbenzeneSteric effectsCyclohexaneSpectral lineMoleculeResonance (particle physics)NMR spectra databaseAlgorithmNuclear magnetic resonanceChemistryPhysicsTheoretical physicsComputer scienceAtomic physicsQuantum mechanicsStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this article, we discuss the progress achieved with the use of evolutionary algorithms for the analysis of 1 H Nuclear Magnetic Resonance spectra of solutes in orientationally ordered liquids. With these tools the analysis of extremely complex spectra that were hitherto impossible to solve has now become eminently feasible. We discuss applications to 2 molecules of special interest: (a) hexamethylbenzene, which is a text book example of steric hindrance between adjacent rotating methyl groups; and (b) cyclohexane which is the standard example of interconversion between various molecular conformations. New interesting physics is obtained in both cases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations6
Published2016
Admission routes1
Has abstractyes

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