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Record W2282278823 · doi:10.1177/0309524x16642082

Computational modeling of wind flow over the University of Calgary campus

2016· article· en· W2282278823 on OpenAlexaffabout
M.A. Mohamed, David Wood

Bibliographic record

VenueWind Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRoofTerrainUniversity campusWind speedWind powerTurbulenceMeteorologyMarine engineeringEnvironmental scienceComputational fluid dynamicsResource (disambiguation)Wind directionWind engineeringFlow (mathematics)Architectural engineeringCivil engineeringEngineeringComputer scienceAerospace engineeringGeographyCartography

Abstract

fetched live from OpenAlex

This paper describes computer simulations of the wind flow over the University of Calgary campus undertaken to assess the likely regions for siting wind turbines and photovoltaic modules. The importation and cleaning of the building geometries is discussed along with the requirements for modeling adjacent buildings on the accurate simulation over one building in particular. The effect of the terrain and the many trees on the campus is shown to be significant for the wind resource over the roof of a six-storey building. It is shown that the choice of turbulence model is not critical if the purpose of the simulation is to identify regions for further exploration via wind speed measurements.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.159
Teacher spread0.154 · 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 designSimulation or modeling
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

Citations5
Published2016
Admission routes2
Has abstractyes

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