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Record W3092254589 · doi:10.1002/app.50037

Development of internal structure of polymer‐modified asphalts via transformations of the reduced frequency

2020· article· en· W3092254589 on OpenAlexaff
Martin Jaššo, Jiri Stastna, Giovanni Polacco, Giacomo Cuciniello

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRheologyProbability density functionPhase angle (astronomy)ThermodynamicsPhase (matter)Function (biology)PolymerMaterials scienceModulusGlass transitionChemistryComposite materialMathematicsPhysicsOpticsOrganic chemistryStatistics

Abstract

fetched live from OpenAlex

Abstract The independent variable of dynamic material functions and corresponding master curves, the reduced angular frequency , was transformed into the apparent molecular weight MW*. As a function of MW*, the normalized master phase angle represented an apparent cumulative molecular weight. This function's numerical differentiation yielded the probability density function of the apparent molecular weight, f(MW*). While this procedure is unproblematic in conventional (straight run) asphalts, it is yielding peaks of negative MW* in polymer modified asphalts. Thus reduced frequency was also transformed to the temperature, which allowed us to determine that the apparent glass transition temperature as the maximum on the master curve of loss modulus G"(T). The master curve of the normalized phase angle as a function of temperature helped to explain the phenomenon associated with the probability density function in modified asphalts. The results presented in this article were obtained numerically, without the use of rheological models.

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 score0.009
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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