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Research on Application of Waterborne Marking Paint with Material Properties

2013· article· en· W2078765682 on OpenAlexaff
Dong Hua Guo

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsWater resistanceForensic engineeringMaterials scienceSkid (aerodynamics)Environmental scienceWaste managementEngineeringCivil engineeringComposite material

Abstract

fetched live from OpenAlex

This paper explains the advantages and shortcomings of waterborne marking paint, based on technical characteristics thereof, describes the physical and chemical properties of said paint required in China, discusses the application status of said paint in China and abroad, and proposes the application trend of said paint in China. The results showed that: the waterborne marking paint has little damages on human and environment because of less volatile organic content (VOC), and has many other advantages such as good retroreflective performance, more conveniently cleaning effect for applying equipment and good skid resistance, etc., while it has the shortcomings of thermal sensitivity, easily freezing performance and metal corrosion, for example; the waterborne marking paint has been used with the third amount based on the total of road marking paint in China, but a further progress needs to be made for the waterborne marking paint in technology, application and market promotion in order to improve its field of application.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.359
Teacher spread0.285 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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