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Record W7051669073

Old buildings, new cities: Analysis of Brussels' Leopold quarter building typologies as a driver to identify optimal retrofitting strategies

2015· article· en· W7051669073 on OpenAlexaboutno aff

Bibliographic record

VenueUPM Digital Archive (Technical University of Madrid) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRetrofittingQuarter (Canadian coin)Identification (biology)Work (physics)
DOInot available

Abstract

fetched live from OpenAlex

In Europe, several studies show that prolonging the life of a building has lower environmental impact than demolishing and building a new one. Retrofitting of residential buildings provides thus a considerable potential in energy conservation and sustainability benefits. But retrofitting an old house is a delicate process. This paper stresses the role of en ergy efficiency retrofitting of old dwellings in Brussels as the key element to achieve the European Union (EU) energy efficiency targets. The approach of this study is to conceive the buildings as a stock rather than individual entities, by developing a preliminary classification by construction system and building comp onents. This approach seeks to contextualize the heritage value, by the identification of the elements that define it, and to achieve holistic improvements of the energy performance of the whole stock in order to highlight the importance and relevance of retrofitting the old residential building sector. The result is a series of scenarios that supposes a first step of the aimed methodology to identify in an early stage the best solutions for this specific part of the building st ock to achieve the energy efficiency targets defined by the Energy Performance of Buildings Directive (EU, 2010)

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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