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Record W2965083695 · doi:10.11159/icert19.123

Energy Savings in an University Educational Building – The Case Of Chemistry Building Of Sapienza

2019· article· en· W2965083695 on OpenAlexvenueno aff
Fabrizio Pini, Sara Verzari, Alessia D’Angelo

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringEnergy (signal processing)Engineering physicsComputer scienceChemistryEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

The commitment for sustainability is a target for the Sustainable Universities Network.Sapienza University of Rome goes in this direction through the energy refurbishment of its building stock, i.e. all the educational buildings.Sapienza campus was built in the 30's and it needs a coherent improvement in terms of energy and environmental performance.Conventional energy retrofitting measures must be within a prioritazion framework to effectively take the most cost-effective strategy.Here, the case of the Chemistry Faculty building is analysed considering the improvement of the building envelope in compliance with the architectural values to be preserved as well as the integration of renewable energy plant.A model of building was used to study it and to create the scenarios and their architectural impact.Furthermore, an energy analysis of the designed refurbishment was carried out to identify the savings.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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