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Record W2792646673 · doi:10.7566/jpscp.21.011022

Exploring the Dynamics of Glasses Using Beta Detected NMR

2018· article· en· W2792646673 on OpenAlexaff
Victoria L. Karner, Tianyi Liu, Iain McKenzie, Aris Chatzichristos, David Cortie, G. D. Morris, R. F. Kiefl, Ryan M. L. McFadden, Zahra Fakhraai, Monika Stachura, W. A. MacFarlane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsTRIUMFUniversity of British Columbia
Fundersnot available
KeywordsBETA (programming language)Dynamics (music)Materials scienceComputer sciencePhysicsProgramming language

Abstract

fetched live from OpenAlex

We report 8Li spin lattice relaxation in two forms of the molecular glass TPD, one a normal glass and one prepared in an ultrastable configuration. The relaxation is remarkably fast, similar to 8Li relaxation in other organic materials and shows a stretched exponential behavior typical of glasses with a small stretching exponent β = 0.3. At low temperature, there is little or no difference between the two glasses, while above 250 K, the relaxation in the denser ultrastable form is faster. In addition, in this temperature range, the data shows a significant thermal hysteresis.

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 score0.075
Threshold uncertainty score0.662

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)0.0010.000

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 head0.102
GPT teacher head0.259
Teacher spread0.157 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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