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Record W2063086379 · doi:10.1002/mrc.2639

Problems, artifacts and solutions in the INADEQUATE NMR experiment

2010· article· en· W2063086379 on OpenAlexafffund
Alex D. Bain, Donald W. Hughes, Christopher Kumar Anand, Zhenghua Nie, Valerie J. Robertson

Bibliographic record

VenueMagnetic Resonance in Chemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of GuelphMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryHeteroatomIsoquinolineCoherence (philosophical gambling strategy)MoleculeCarbon chainOrganic moleculesComputational chemistryChemical physicsBiological systemStereochemistryOrganic chemistryQuantum mechanicsRing (chemistry)

Abstract

fetched live from OpenAlex

The INADEQUATE experiment can provide unequalled, detailed information about the carbon skeleton of an organic molecule. However, it also has the reputation of requiring unreasonable amounts of sample. Modern spectrometers and probes have mitigated this problem, and it is now possible to get good structural data on a few milligrams of a typical organic small molecule. In this paper, we analyze the experiment step by step in some detail, to show how each part of the sequence can both contribute to maximum overall sensitivity and can lead to artifacts. We illustrate these methods on three molecules: 1-octanol, the steroid 17alpha-ethynylestradiol and the isoquinoline alkaloid beta-hydrastine. In particular, we show that not only is the standard experiment powerful, but also a version tuned to small couplings can contribute vital structural information on long-range connectivities. If the delay in the spin echo is long, pairs of carbons with small couplings can create significant double-quantum coherence and show correlations in the spectrum. These are two- and three-bond correlations in a carbon chain or through a heteroatom in the molecule. All these mean that INADEQUATE can play a viable and important role in routine organic structure determination.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.922

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2010
Admission routes2
Has abstractyes

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