Use of KROQI as a Level-2 Common Data Environment in the French Construction Industry
Bibliographic record
Abstract
The implementation of a common data environment (CDE) in the construction industry appears to be crucial for organizing and managing the flow of important information within construction projects effectively. However, it is observed that the investments and the costs associated with setting up such environments are often dissuasive, particularly for small- and medium-sized enterprises (SMEs). By launching the Plan BIM 2022 in 2017, the French government aimed to combat the emerging digital divide between large and small construction companies. At the heart of this state-funded plan was the development of a CDE-like collaborative platform called KROQI. The work presented in this article proposes a three-level matrix to characterize common data environments based on computer-supported collaboration work theories. The paper demonstrates how KROQI can be characterized as a level-2 CDE solution. Based on a preliminary study, it explores the perception and satisfaction of practitioners with respect to the features proposed by KROQI. The results show that, among the large variety of features proposed, some are hardly ever used. Therefore, while KROQI appears to be an excellent industry-wide solution aimed at federating initiatives towards digital collaboration centered around BIM models, there is definitely room for improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".