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Record W2116723157 · doi:10.1002/anie.201004202

Inside Cover: Solid‐State <sup>17</sup>O NMR Spectroscopy of Large Protein–Ligand Complexes (Angew. Chem. Int. Ed. 45/2010)

2010· paratext· en· W2116723157 on OpenAlexaff
Jianfeng Zhu, Eric Ye, Victor V. Terskikh, Gang Wu

Bibliographic record

VenueAngewandte Chemie International Edition · 2010
Typeparatext
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular SciencesUniversity of OttawaQueen's University
Fundersnot available
KeywordsNuclear magnetic resonance spectroscopySolid-state nuclear magnetic resonanceSpectroscopyCover (algebra)Ligand (biochemistry)Solid-stateChemistryCrystallographyNMR spectra databasePhysical chemistrySpectral lineMaterials scienceAnalytical Chemistry (journal)Nuclear magnetic resonancePhysicsStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Large biomolecular systems have been probed for the first time by solid-state 17O NMR spectroscopy. In their Communication on page 8399 ff., G. Wu and co-workers show that high-quality solid-state 17O NMR spectra can be obtained for large protein–ligand complexes at an ultrahigh magnetic field of 21 T. The sensitivity of solid-state 17O NMR experiments at this field should allow biomolecular systems of up to 300 kDa in size to be tackled.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.388
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3880.259

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 head0.012
GPT teacher head0.301
Teacher spread0.288 · 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
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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