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Peculiarities Research of Buildings and Structures Energy Efficiency

2022· article· en· W4213136729 on OpenAlexaboutno aff
A.A. Yudin, А. Р. Бикташева, А И Габитов, A S Salov

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear decommissioningArchitectural engineeringEfficient energy useWork (physics)Quality (philosophy)Computer scienceConstruction engineeringCivil engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The constant buildings growth inevitably increases volume and work cost on their technical diagnosis. This defines the relevance of introducing non-destructive testing modern methods, which accelerate diagnosis, obtain a reliable assessment of technical condition and residual resource reasonable prognostication in safe operation field. Priority are control methods that do not require facility decommissioning, which provides a significant time and money reduction. Currently, infra-red thermal imaging using interest has significantly increased. This is due to the adoption of Russian Federation regulatory documents on improving energy efficiency and energy-saving technologies introduction in the construction and buildings reconstruction. Sweden, Canada and the United States developed a significant number of standards and guidelines for practical examinations of buildings and structures using thermal imaging quality control methods of building constructions thermal insulation at the end of the last century. On the other hand, relatively inexpensive matrix detectors of infrared radiation have been developed and put into widespread use, as a result of measurement models have become available. In the Bashkortostan Republic based on the analysis of thermal imaging studies of housing, civil and industrial construction projects, energy-efficient building construction have been developed and proposed.

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 score0.045
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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
Published2022
Admission routes1
Has abstractyes

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