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COMPARISON OF DETERMINATION OF SNOW LOADS FOR ROOFS IN BUILDING CODES OF VARIOUS COUNTRIES

2021· article· en· W3207021822 on OpenAlexaboutno aff
Alexander M. Belostotsky, Nikita Britikov, Oleg Goryachevsky

Bibliographic record

VenueInternational Journal for Computational Civil and Structural Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicStructural mechanics and materials
Canadian institutionsnot available
Fundersnot available
KeywordsSnowNormativeRoofMoment (physics)European standardSet (abstract data type)Snow coverEuropean unionMathematicsEnvironmental scienceCivil engineeringComputer scienceGeographyMeteorologyEngineeringArchitectural engineeringPolitical scienceBusinessLawPhysics

Abstract

fetched live from OpenAlex

The article compares the requirements for calculating the snow load on the coatings of buildings and structures in accordance with the regulations of technically developed countries and associations – Russia, the European Union, Canada and the United States. It was revealed that in these norms the general approaches, the subtleties of calculating the coefficients, the set of standard coatings and the schemes of the form coefficient proposed for them differ significantly. This situation reflects the general problem of determining snow loads – at the moment there is no recognized unified scientifically grounded approach to determining snow loads on coatings of even the simplest form. The difference in the normative schemes of snow loads is clearly demonstrated by the example of a three-level roof.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.323
Teacher spread0.307 · 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 designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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