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Record W4313072567 · doi:10.1051/shsconf/202214701002

Les environnements communs de données (CDE) : définitions, historique et classification

2022· article· fr· W4313072567 on OpenAlex
Élodie Hochscheid, Conrad Boton, Louis Rivest

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSHS Web of Conferences · 2022
Typearticle
Languagefr
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Le Building Information Modeling (BIM) est une innovation qui regroupe un ensemble de méthodes, processus et outils de travail permettant d’alimenter et d’exploiter des informations d’un ouvrage bâti tout au long de son cycle de vie. Les CDE ( Common Data Environment ) sont des espaces numériques ou plateformes qui supportent les processus BIM. Ils offrent des ensembles de services numériques qui permettent de stocker, gérer et échanger des données de natures variées. Plusieurs initiatives récentes les positionnent au coeur du développement de l’interopérabilité dans les pratiques BIM en initiant la standardisation de leurs fonctionnalités. Dans cet article, nous revenons sur la notion de CDE et son origine ainsi que sur les initiatives récentes qui mettent en évidence les enjeux des CDE. Malgré leur standardisation en cours, notre recherche met en évidence leur grande diversité. Nous proposons une classification qui permet d’appréhender cette hétérogénéité plus facilement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.995

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)0.0060.000

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 head0.057
GPT teacher head0.259
Teacher spread0.203 · 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