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

2021· article· en· W3207021822 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.229
Threshold uncertainty score0.332

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 studies0.0000.000
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 head0.016
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