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Record W4226040714 · doi:10.33774/miir-2022-zvqtx

Analyzing viscoelastic materials

2022· preprint· en· W4226040714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsViscoelasticityProduct (mathematics)Nonlinear systemSpring (device)Product lineSet (abstract data type)sortComputer scienceLine (geometry)ViscosityMathematicsApplied mathematicsMechanical engineeringEngineeringPhysicsManufacturing engineeringGeometryThermodynamicsInformation retrieval

Abstract

fetched live from OpenAlex

Both deterministic modelling and data analysis are used to characterize a product line of viscoelastic cushions. Classical Kelvin-Voigt and Maxwell models are derived and solved to compare with industry supplied experimental data. A Maxwell model with a nonlinear spring is used to extract an effective viscosity for each product and used to sort the product line. Data analysis techniques are used to find a small set of exemplar cushions, chosen based on their extremal behaviour in the supplied data. These are then used to characterize all the products. Both methods provide a unique way to classify the product offerings in the marketplace.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.995

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)0.0060.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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