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Snow Friction Coefficient for Commercial Roofing Materials

2017· article· en· W2751880582 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cold Regions Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSnowGeotechnical engineeringEnvironmental scienceCoefficient of frictionSnow coverGeologyMaterials scienceGeomorphology

Abstract

fetched live from OpenAlex

Redistribution of snow loads on two adjacent buildings of different heights is addressed. The term slippery roofs is used but it is not defined. Measurements of friction between snow and commercial roofing materials are carried out to help specify the term. In order to determine a static coefficient of friction, the Coulomb friction principle of measuring inclination increase until specimen movement begins is applied. A test apparatus is designed and produced. Single-ply roofing materials such as membranes made of polyvinyl chloride (PVC), thermoplastic polyolefin (TPO), ethylene propylene diene monomer (EPDM), and modified bitumen (MB), as well as painted industrial metal profile specimens are tested. Snow specimens from fresh snow are prepared and tests are carried out under laboratory conditions. For the given conditions, although deviating from full-scale realistic conditions, snow samples slid at different angles depending on the roofing material type. Average values of sliding angle for PVC, TPO, EPDM, and MB membranes and metal sheet are determined to be 6°, 15°, 20°, 57°, and 22°, respectively. Based on the published provisions and given test conditions, commercial roofing materials can be categorized as slippery.

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 score0.319
Threshold uncertainty score0.349

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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