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Record W4249327812 · doi:10.26868/25222708.2019.210189

On the Prediction of Ground-Reflected Solar Radiation and its Relevance in the Context of Building Performance Simulation (BPS)

2020· article· en· W4249327812 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

VenueBuilding Simulation Conference proceedings · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsCarleton University
Fundersnot available
KeywordsRelevance (law)Context (archaeology)Computer scienceRadiationAerospace engineeringArchitectural engineeringEnvironmental sciencePhysicsEngineeringGeologyOptics

Abstract

fetched live from OpenAlex

Establishing accurate ground reflectivity values is critical for the reliable prediction of ground-reflected solar radiation. It is common for building performance simulation users to employ the default values of ground reflectivity (0.2 for most simulation tools) which can lead to significant inaccuracies, especially for periods during which the ground is covered by snow. The paper provides an overview of existing models for predicting ground reflectivity, and then presents a new model suitable for implementation into BPS tools. Simulation results predicted with the new model are further compared with measurements. To quantify the influence of the new model, two sets of simulations were run with ESP-r to predict the solar irradiance incident on a south oriented façade and on a south oriented 60°-sloped surface. Comparing the predictions with measured data, it was noticed that the influence of the new model on predicted solar irradiance is higher for the vertical surface.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.284
Teacher spread0.222 · 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