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Record W2102111251 · doi:10.5539/mas.v8n5p115

Structural Dynamic Study of Roof Waterproofing Materials

2014· article· en· W2102111251 on OpenAlexvenueno aff
А. Ф. Кемалов, Ruslan Alimovich Kemalov, Dinar Z. Valiev, Ilmira Maratovna Abdrafikova

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersKazan Federal University
KeywordsAsphaltMaterials sciencePolymerRaw materialComposite numberComposite materialProcess engineeringChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The present research was aimed to develop the scientific applied principles and technologies of composite bituminous materials for civil engineering based on the investigation of the structures of polymer modifier and bitumen-polymer binder (BPB) on its basis with the use of nuclear magnetic resonance (NMR). The method of pulsed NMR was chosen as one of the rapid analysis methods that can be used for the analysis of bitumen-polymer systems, especially when assessing the group chemical composition of residual oil feedstock (ROF), bitumens and composite materials based on them. Using the method of pulsed NMR the regularities of the impact of modifier component composition on the changes of structural-group composition of the original and modified products were specified. Based on the results of research the optimal ratio of bitumen-polymer binder components was investigated, the manufacturability of the process for obtaining of composite bituminous materials for civil engineering with the aim of optimizing the quality of the final products was evaluated. Pulsed NMR - spectroscopy is suggested as input and output quality control of bituminous products. The regularities of redistribution of the phases with different molecular mobility and their relationship with the binder components were investigated. Rapid technique for quantifying the content of polymer in the solvent was developed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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