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Record W7118980847 · doi:10.25545/wgnufh

Dynamic mechanical analysis with portable NMR

2025· dataset· W7118980847 on OpenAlexaff
William Selby, Phil Garland, Igor V. Mastikhin

Bibliographic record

VenueUNB Dataverse · 2025
Typedataset
Language
Field
Topic
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMeasure (data warehouse)VibrationAmplitudeDynamic mechanical analysisPhase (matter)Dynamic modulusData acquisitionRheologySample (material)

Abstract

fetched live from OpenAlex

This repository contains all raw data files and processing scripts associated with the paper published in the Journal of Magnetic Resonance entitled “Dynamic Mechanical Analysis with Portable NMR.” Abstract: Dynamic mechanical analysis (DMA) is an umbrella term for a variety of rheological experiments in which the response of a sample subjected to an oscillatory force is measured to characterize its dynamic properties. In this work, we present a method for DMA that employs a small unilateral three magnet array with an extended constant gradient to measure the velocity of a vibrating sample. By orienting the vibrations in the direction of the gradient, we use the motion-sensitized phase accumulation to determine the velocity. By implementing delays into the pulse sequence, we measure the phase at evenly spaced points in the vibration cycle, allowing for the acquisition of a complete velocity waveform. Using velocity waveforms, samples are characterized through differences in amplitude and phase, providing information on the magnitude of the dynamic modulus and loss-angle, respectively.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.097
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0070.020
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0060.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0690.064

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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