Bridging the knowledge gaps to promote more environmentally sustainable buildings - It begins at the foundation
Bibliographic record
Abstract
Toronto has recognized building structures as an \nintegral tool for reducing its carbon footprint. Yet \na paramount concern, however, is whether the \nbuilding and construction industry is prepared and \npositioned to effectively design and communicate \nthe importance of sustainable buildings. The decisions \nduring the pre-design stage of new building \ndevelopments are vital in affecting the project’s \noverall sustainability. The plan for transitioning \nand promoting more environmentally sustainable \nbuildings is hard to define; however, using design \nthinking, systems thinking, and strategic foresight, \nthis report has clarified a pathway by focusing on \nknowledge management. This report concludes \nby offering three areas to strengthen knowledge \nbuilding and management capabilities within the \nbuilding and construction industry.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".