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Record W2995661869

Membrane solid-state NMR in Canada: A historical perspective Proteins and proteomics

2017· article· en· W2995661869 on OpenAlexaboutno aff
Michéle Auger

Bibliographic record

VenueBiochimica et Biophysica Acta · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLipid bilayerMembrane proteinSolid-state nuclear magnetic resonanceChemistryMembraneContext (archaeology)Biological membraneModel lipid bilayerBiophysicsBiochemistryPhysicsBiologyNuclear magnetic resonanceLipid bilayer phase behavior
DOInot available

Abstract

fetched live from OpenAlex

This manuscript presents an overview of more than 40years of membrane solid-state nuclear magnetic resonance (NMR) research in Canada. This technique is a method of choice for the study of the structure and dynamics of lipid bilayers; bilayer interactions with a variety of molecules such as membrane peptides, membrane proteins and drugs; and to investigate membrane peptide and protein structure, dynamics, and topology. Canada has a long tradition in this field of research, starting with pioneering work on natural and model membranes in the 1970s in a context of emergence of biophysics in the country. The 1980s and 1990s saw an emphasis on studying lipid structures and dynamics, and peptide-lipid and protein-lipid interactions. The study of bicelles began in the 1990s, and in the 2000s there was a rise in the study of membrane protein structures. Novel perspectives include using dynamic nuclear polarization (DNP) for membrane studies and using NMR in live cells. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

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 score0.204
Threshold uncertainty score0.811

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.271
Teacher spread0.257 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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