Rapid Assessment of Snow Drifting Conditions Using Physical Model Simulations
Bibliographic record
Abstract
Methods for evaluating snow loads on roofs are described within building standards and codes such as American Society of Civil Engineers 7 and the National Building Code of Canada. These recommendations are often simplified and generic in order to be applicable to most structures and, as a result, do not account for the unique aerodynamics that can occur around a structure. In addition, these codes and standards do not address serviceability concerns, such as accessibility to building entrances, nor identify where mechanical air intakes may be prone to snow ingestion as is common to buildings located in cold regions. Scale model testing within an open channel water flume allows for the rapid assessment of the building aerodynamics and snow drifting conditions for the purposes of design. This paper presents snow accumulation patterns representing a snowfall event from a single direction for a building with commonly seen geometries including roof steps, arched roofs, and a projecting tower with mechanical penthouse. Multiple test directions are presented to illustrate the influence of wind directionality and resulting building aerodynamics on snow accumulations at grade level, on roof areas, and where mechanical intake equipment is often sited.Parapets, canopies, and recommendations for mechanical air intakes for reducing problematic snow accumulations are presented and discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".