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Record W4231078987 · doi:10.3410/f.735298037.793575273

Faculty Opinions recommendation of Aromatic 19F-13C TROSY: a background-free approach to probe biomolecular structure, function, and dynamics.

2020· dataset· en· W4231078987 on OpenAlexfundno aff
Ayyalusamy Ramamoorthy

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2020
Typedataset
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilNational Institutes of HealthUniversity of TorontoAustrian Science Fund
KeywordsFluorine-19 NMRRelaxation (psychology)Nuclear magnetic resonance spectroscopyBiomoleculeSpectroscopyChemistryFunction (biology)Nuclear magnetic resonanceChemical physicsPhysicsPsychologyBiologyNeuroscienceQuantum mechanics

Abstract

fetched live from OpenAlex

Obtaining atomic level information about the structure and dynamics of biomolecules is critical to understand their function.Nuclear magnetic resonance (NMR) spectroscopy provides unique insights into the dynamic nature of biomolecules and their interactions, capturing transient conformers and their features.However, relaxation-induced line broadening and signal overlap make it challenging to apply NMR to large biological systems.Here, we take advantage of the high sensitivity and the broad chemical-shift range of 19 F nuclei, and leverage the remarkable relaxation properties of the aromatic 19 F-13 C spin pair to disperse 19 F resonances in a 2dimensional transverse relaxation optimized TROSY spectrum.We demonstrate the application of the 19 F-13 C TROSY to investigate proteins and nucleic acids.This experiment expands the scope Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.195
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1950.116

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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical LiteratureSame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207