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Record W1966366621 · doi:10.5539/mas.v9n1p45

Green Composites in Architecture and Building Material Science

2014· article· en· W1966366621 on OpenAlexvenueno aff
R.V. Lesovik, Yury Degtev, Mahmud Shakarna, Anastasiya Levchenko

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsRaw materialBuilding materialRubbleNatural materialsQuality (philosophy)Filler (materials)Architectural engineeringArchitectureProduction (economics)PressingComputer scienceConstruction engineeringEnvironmental scienceCivil engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Currently, the topic of improvement of man`s live ability is becoming increasingly important. The notion of luxury living in the city includes social comfort, environment comfort (urban, natural landscape component). A wide range of small architectural forms of different architectural design and purpose is developed. The basic material for the production of small architectural forms is concrete. On optimal combination of negative and positive qualities, concrete is the most cost- effective material. In order to avoid increasing the price of hardscape, at their creating, it`s actual to use local raw materials and industrial waste. On their basis the modern high quality building materials are developed. To reduce prime costs of construction materials the technogenic raw materials are used. The solution of this actual problem possibly on the basis of expansion of a source of raw materials of the stone materials suitable for production of filler. Use of fine-grained concrete is perspective. This look allows to receive high-quality structure of a material, differs high technological effectiveness, allowing it is rather simple to make products as a pressing method with immediate striking, and it is easily and effectively modified by a molding method that is especially convenient for monolithic housing construction, and, besides, by means of organic-mineral modifiers, providing materials with various use of cheap local sand that allows to reduce concrete cost by 15-20% in comparison with coarse-grained concrete on rubble.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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
Published2014
Admission routes1
Has abstractyes

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