MétaCan
Menu
Back to cohort
Record W6968329572 · doi:10.5281/zenodo.14234964

REHOUSE public report: LEVEL(s)-based MEL framework for REHOUSE RPs and demos

2024· article· en· W6968329572 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsCanadian Anesthesia Research Foundation
FundersEuropean Commission
KeywordsPerformance indicatorScale (ratio)Domain (mathematical analysis)Work (physics)Performance measurement

Abstract

fetched live from OpenAlex

Summary: This document represents the REHOUSE report D3.1 “LEVEL(s)-based MEL framework for REHOUSE RPs and demos” developed within work page (WP) 3 (Measurement, evaluation and learning methodology, impact assessment and platforms specifications). It presents firstly LEVEL(s) [1] framework that defines methods on how to assess key performance indicators (KPIs) for the evaluation of environmental performance of both renovations and new buildings. To complement this framework, an inventory of technical and social KPIs defined in other frameworks are presented: Regional/National/International regulations and standards; Scientific articles/ technical studies; Experience feedback from partners’ previous projects; Social frameworks. The frameworks identified cover the countries of REHOUSE demonstration sites (Italy, Greece, Hungary and France) as well as include international context. Although the inventory of frameworks included an important number of regulations, studies and protocols, only the most complete and interesting ones have been incorporated in this deliverable. Finally, a summary of KPIs from all these sources is established and classified in order to guide the further selection of the suitable KPIs for REHOUSE Renovation Packages and demonstration sites. To be in adequacy with the objectives of the REHOUSE project and the European LEVEL(s) framework the summary of KPIs is arranged by 3 criteria:1. Scale. The KPIs are given at the scale of renovation packages, buildings and at the scale of territory/city.2. Macro-objectives (MO) of the LEVEL(s) framework.3. Category. The KPIs are classified by domain related to construction projects. The links between these classifications are illustrated in the report. The buildings’ KPI are mainly based on the KPIs from the LEVEL(s) framework augmented with the results of the MEL inventory by REHOUSE project partners which together with Renovation Packages’ KPIs create a basis for the definition of the REHOUSE project KPIs in the task 3.2. The results of the MEL inventory by project partners allow also to identify some additional KPIs which are not considered by the LEVEL(s) framework. For the purposes of KPIs definition of the REHOUSE project in the Task 3.2 all the KPIs have been classified by the next categories: Building envelope. Here are included indicators related to thermal parameters of building walls and roof. Resource use. This category integrates indicators related to building energy and water consumption, as well as the amount of waste generated. Comfort conditions. It integrates indicators related to thermal, acoustic and visual comfort as well as the IAQ. Economics. It integrates indicators related to the cost dimension of construction process. Social/user’s behaviour. This category includes indicators related to the evaluation of impact of building use on life of users and changes in users’ behaviour inside or outside the building/dwelling they live. Environment and built environment. It integrates indicators related to greenhouse gas emissions of buildings in operational phase, as well as those associated to the outdoor environment of building or construction site. Resources re-use. This category includes indicators related to recycling and reuse of materials, wastes and other goods. Further public reports of the REHOUSE project: Publications – REHOUSE

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.269
Teacher spread0.210 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicSustainable Building Design and AssessmentFrench-language works237,207