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Record W2049572097 · doi:10.1021/bm0342685

A DECODER NMR Study of Backbone Orientation in <i>Nephila </i><i>c</i><i>lavipes</i> Dragline Silk under Varying Strain and Draw Rate

2004· article· en· W2049572097 on OpenAlexaff
Philip T. Eles, Carl A. Michal

Bibliographic record

VenueBiomacromolecules · 2004
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSILKStrain (injury)Orientation (vector space)ChemistryMaterials scienceNanotechnologyStereochemistryBiologyAnatomyGeometryMathematicsComposite material

Abstract

fetched live from OpenAlex

Using DECODER (direction exchange with correlation for orientation distribution evaluation and reconstruction) NMR, we probe the orientations of carbonyl carbons in [1-(13)C]glycine-labeled dragline silk under conditions of varying strain and fiber draw rate. A model-specific reconstruction of the molecular orientation distribution incorporating beta sheets and polyglycine II helices indicates that the structures' alignment along the fiber can be described by a pair of Gaussian distributions with full width at half-maxima of 20 and 68 degrees and approximately 45 and approximately 55% relative contributions to the signal intensity. The alignment along the fiber was found to change appreciably when the drawing tension on the fiber was relaxed in a sample drawn at 4 cm/s while little change was observed in a sample drawn at 2 cm/s. The degree of alignment along the fiber was found to increase with fiber draw rate.

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 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.273
Teacher spread0.258 · 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 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

Citations45
Published2004
Admission routes1
Has abstractyes

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