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Record W7024915368

The Third Succes Factor of Renovations with Energy Ambitions

2016· article· en· W7024915368 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2016
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGRASPEnergy (signal processing)Success factorsSustainable developmentFactor (programming language)Sustainable energyEfficient energy use
DOInot available

Abstract

fetched live from OpenAlex

Everyone acknowledges the importance of sufficient financial resources and wellfunctioning technologies when it comes to renovation processes with energy ambitions. However, Dutch experiences show that these two factors alone do not automatically result in success. More is needed, and this ‘more’ has to do with less concrete, but in the existing living environment very influential factors such as emotions and wellbeing. This hard to grasp factor is called ‘the third success factor of energy friendly renovation processes’. In the Netherlands, the questions ‘What exactly is this third success factor?’ and ‘How to integrate knowledge about this third success factor in the rational-oriented building industry?’ have been put high on the agenda of people who want to achieve the national energy goals related to the built environment. Research that combines knowledge from human sciences with energy efficient renovation experiences, the development of new educational methods, and a search for success stories has been part of joined efforts to find answers to these questions. A national knowledge platform called ‘HomeMates’ has been established to bundle and share all these findings. The Dutch experiences are described and discussed in this paper. They are also linked to Canadian experiences, based on the results of a project of Parallel52⁰ , the Dutch Canadian Sustainable Building and Planning network. In this project, Dutch findings and findings in the Toronto area were compared and discussed.\n\nAwarded for best paper

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2016
Admission routes1
Has abstractyes

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