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Record W2030858841 · doi:10.1002/cmr.a.20132

Studying natural structural protein fibers by solid‐state nuclear magnetic resonance

2009· article· en· W2030858841 on OpenAlexafffund
Alexandre A. Arnold, Isabelle Marcotte

Bibliographic record

VenueConcepts in Magnetic Resonance Part A · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesCentre québécois sur les matériaux fonctionnels
KeywordsSolid-state nuclear magnetic resonanceOrientation (vector space)Solid-stateFiber diffractionSpider silkMaterials scienceElastinMolecular dynamicsChemical physicsDiffractionBiological systemChemistryPhysicsX-ray crystallographyNuclear magnetic resonanceComputational chemistrySILKComposite materialMathematicsOpticsPhysical chemistryBiologyGeometry

Abstract

fetched live from OpenAlex

Abstract As a consequence of evolutionary pressure, various organisms have developed structural fibers displaying a range of exceptional mechanical properties adapted specifically to their functions. An understanding of these properties at the molecular level requires a detailed description of local structure, orientation with respect to the fiber and size of constitutive units, and dynamics on various timescales. The size and lack of long‐range order in these protein systems constitute an important challenge to classical structural techniques such as high‐resolution NMR and X‐ray diffraction. Solid‐state NMR overcomes these constraints and is uniquely suited to the study of these inherently disordered systems. Solid‐state NMR experiments developed or applied to determine structure, orientation, and dynamics of these complex proteins will be reviewed and illustrated through examples of their applications to fibers such as spider and silkworm silks, collagen, elastin, and keratin. © 2009 Wiley Periodicals, Inc. Concepts Magn Reson Part A 34A: 24–27, 2009.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.270
Teacher spread0.260 · 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 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

Citations11
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueConcepts in Magnetic Resonance Part ASame topicSilk-based biomaterials and applicationsFrench-language works237,207