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Record W2083418470 · doi:10.1002/bip.10155

Unusual swelling of elastin

2002· article· en· W2083418470 on OpenAlexaff
M. A. Lillie, John M. Gosline

Bibliographic record

VenueBiopolymers · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSwellingElastinChemistrySwellVolume (thermodynamics)Thermal expansionIsotropyThermodynamicsChemical engineeringPolymer chemistryComposite materialMaterials science

Abstract

fetched live from OpenAlex

The swelling behavior of the elastin network has been investigated by comparing the linear expansion of samples of purified elastin with the volume expansion of the network, calculated on the basis of composition. Elastin sample dimensions and sample masses were measured under three conditions in which volume changes: thermal expansion at fixed water contents, deswelling due to dehydration, and swelling to greater than normal levels due to the swelling agent, sodium dodecyl sulfate. Isotropic network swelling usually changes length in proportion to the cube root of network volume, but length was found to be directly proportional to volume, showing a greater increase in length than expected. This unusual swelling behavior is attributed to an unusual elastin structure at the subfiber level, but there is insufficient detail on elastin's molecular organization to identify a mechanism to explain how it occurs. Assuming the network swells homogeneously, we describe two models that correctly predict swelling behavior, but these models imply a significant deviation from the structure generally assumed for an elastomeric polymer network of kinetically free molecular chains. Assuming that the network swells heterogeneously removes part of the difficulty with the models, but the observed direct proportionality between length and network volume remains to be explained.

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 categoriesInsufficient payload (model declined to judge)
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 score0.329
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.232
Teacher spread0.220 · 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

Citations16
Published2002
Admission routes1
Has abstractyes

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