MétaCan
Menu
Back to cohort
Record W2357926291

Analysis on Energy Consumption and Economy of an Office Building Equipped with Low-E Window in Beijing

2008· article· en· W2357926291 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBuilding Science · 2008
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWindow (computing)BeijingInvestment (military)Consumption (sociology)Energy consumptionPower consumptionQuarter (Canadian coin)Environmental sciencePower (physics)EngineeringComputer scienceElectrical engineeringGeographyOperating systemPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In this paper,the energy efficiency performance and heat transfer model of Low-E glass were firstly introduced.And then,under six different kinds of glass window conditions,the software of DeST-c was used to simulate the hourly indoor air temperature,energy consumption of an office building in Beijing,also the influences of different seasons and orientations on window types were compared.Thirdly,based on the analysis of energy consumption,the investment and operation cost of the selected window,such as single and assembled windows,and common double window were analyzed and compared.The results indicated that the investment of the assembled window was 59.39% higher than that of the normal one,while the power of 7.42% could be saved each year.As a result,the overspend part of the investment could be took back in only two years.In a conclusion,with the relatively lower investment risk,the assembled window would be the first choice of this office building.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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