Utility Benchmarking And Potential Savings of Multi-Unit Residential Buildings (MURBs) In Toronto
Bibliographic record
Abstract
Building energy, water and solid waste benchmarking and developing meaningful performance indicators can help government and building owners to make effective decisions on improving their buildings' efficiency. For this study, information on 120 Multi-Unit Residential Buildings (MURBs) has been provided. The study entails the following steps: performing energy (weather normalized using the Princeton Scorekeeping Method (PRISM)), water, and solid waste benchmarking for the 120 MURBs, developing meaningful performance indicators; determining performance ranking; and estimating different levels of savings (energy, water, solid waste, cost, and GHG emissions). The most appropriate performance indicator and the benchmarking range for energy, water, and solid waste are as follows, energy consumption normalized by square metre, 141-580 kWh/m
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".