Usage of Building Information Modeling for Sustainable Development Education
Bibliographic record
Abstract
and a Young Energy Professional (YEP) ascribed to the Energy Council of Canada (ECC).Benjamin's research is focused in the development and implementation of emergent technologies (BIM, CIM, IoT, Big Data, Machine learning, LCA, 3D scan to BIM) for increasing the performance of construction building projects in terms of sustainability and Circular Economy.Benjamin has done recent contributions on international journals for the valuation and monetization of the environmental impacts of the residual life of building stock in North America.His contributions add a Life Cycle Assessment (LCA) perspective to the decision-making methodology involved in adaptive reuse of buildings, in order to contribute to sustainability and climate change through mitigation of CO2 emissions.Benjamin is a Civil Engineer with a doctorate in Civil Engineering from the University of Waterloo in Ontario, Canada.He is originally from the city of Puebla in Mexico.Before initiating his doctoral studies, he worked as infrastructure construction supervisor and environmental inspector of the State of Puebla.Puebla is the fourth largest state in Mexico with 6.1 million inhabitants.Some of his duties were verifying the fulfillment of the applicable laws inside of the construction and environmental jurisdiction for new and existing buildings.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".