openBIM implementations for a Canadian roadmap
Bibliographic record
Abstract
This comprehensive research project investigates the transformative journey of the Canadian architecture, engineering, construction, owner, operator, and municipality (AECOOM) sectors as they adopt Building Information Modeling (BIM) and openBIM standards to digitalize the planning, design, construction, and management of built assets. With the digitalization of the AECOOM through BIM and data exchange standards, Canada aims to unlock decarbonization aspirations and requirements, optimize project outcomes, and lead in sustainable and digitized built asset industry. Internationally, the adoption of openBIM standards and practices is fostering a more collaborative, transparent, and efficient development lifecycle. Canada's focus on interoperability and the ability for data to be exchanged across various software platforms positions it to benefit from enhanced digital workflow, and more reliable outcomes. By reviewing and engaging with buildingSMART International (bSI) chapters and other nations' approaches, this research project provides a comprehensive analysis, highlighting significant commonalities, patterns, and differences in openBIM methodologies. As the industry marches towards a more digital, collaborative, and sustainable future, Canada stands to gain immensely from the lessons learned by other jurisdictions. By focusing on government initiatives, collaboration, education, and standardization, the country can position itself at the forefront of BIM and openBIM innovation, driving economic growth while fulfilling its environmental and societal commitments. The research project highlights the successful implementation of BIM within the AECOOM sector, offering benefits such as greater efficiency, reduced costs, enhanced collaboration, and improved decision-making. It also outlines the pivotal project undertaken in collaboration with buildingSMART Canada (bSC) and various stakeholders within the AECOOM sector to assess the readiness of provinces and territories to adopt BIM and enable comprehensive standards and processes for its continued implementation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.016 |
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".