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Record W3007991909 · doi:10.1080/19401493.2020.1728383

A model for predicting the solar reflectivity of the ground that considers the effects of accumulating and melting snow

2020· article· en· W3007991909 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Building Performance Simulation · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCarleton University
Fundersnot available
KeywordsSnowReflectivityEnvironmental scienceMeteorologyAtmospheric sciencesRemote sensingGeologyOpticsPhysics

Abstract

fetched live from OpenAlex

Simulation tools for predicting building thermal performance and solar system performance must accurately calculate solar irradiance to surfaces of arbitrary orientation. This is imperative to correctly predict passive solar gains to buildings and to accurately estimate thermal and electrical production of solar collectors. In cold climates, where snow covers the ground for long periods of time, ground reflected radiation can represent a substantial fraction of the total incident irradiance to highly tilted and vertical surfaces (e.g. windows). A new model has been developed to improve the calculation of ground-reflected radiation in simulation tools. The model is based upon empirical observations taken at a measurement site in Ottawa (Canada), and has been validated using disjunct data from the measurement site, and with published data from two other sites in the USA. The model was found to increase the accuracy of ground reflectivity predictions for cold and humid climates.

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)0.001
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 score0.039
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.073
GPT teacher head0.282
Teacher spread0.209 · 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