MétaCan
Menu
Back to cohort
Record W1965534359 · doi:10.1061/9780784478462.007

Poisson's Ratio of Hot Asphalt Mixtures Determined by Relaxation and Small Amplitude Oscillation Tests

2014· article· en· W1965534359 on OpenAlexaff
Josef Žák, Jiri Stastna, Jiri Vavricka, Kristýna Miláčková, Lukas Kasek, Ludo Zanzotto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPoisson's ratioPoisson distributionIsotropyViscoelasticityAmplitudeMaterials scienceOscillation (cell signaling)Relaxation (psychology)MechanicsTransverse planeConstant (computer programming)Mathematical analysisMathematicsPhysicsComposite materialStructural engineeringStatisticsOpticsComputer scienceEngineeringChemistry

Abstract

fetched live from OpenAlex

One of the basic material characteristics of solids is Poisson's ratio. Its exact knowledge, including its dependence on time, allows us to model the effect of load, with given boundary conditions, on the behavior of material under consideration. Poisson's ratio, or rather the relationship between the longitudinal and transverse strain, has an important implication in engineering mechanical applications (assessment of pavement structure performance). Currently, Poisson's ratio usually is used as a constant, based on an incorrect assumption that a hot-asphalt mix (HMA) is a linear elastic material. This paper reports Poisson's ratio as a function of time determined from the relaxation and small amplitude oscillation tests on cylindrical specimens. An HMA is considered homogeneous isotropic material, and linear theory of viscoelasticity is applied for the experimental determination of the lateral contraction ratio.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicAsphalt Pavement Performance EvaluationFrench-language works237,207