Energy consumption analysis of school buildings in Manitoba, Canada
Bibliographic record
Abstract
Buildings contribute 20–40% of the world’s energy consumption, making the need to investigate their energy performance a necessity. Given the lack of empirical evidence on the energy performance of school buildings in cold climates, this study aimed to benchmark historical energy consumption over a ten-year period in a sample of 30 school buildings in Manitoba, Canada. Results showed the median total energy consumption of these schools was higher than other Canadian benchmarks. School building age had a statistically significant effect on their energy consumption, with newer schools consuming less gas but more electricity than older and middle-aged ones. The retrofits implemented in some schools did not for the most part have a statistically significant effect on their energy consumption, although a decrease in energy consumption was observed. The results also showed that middle-aged schools were the largest energy consumers, with the results changing depending on the metric used to report on schools’ energy consumption, reinforcing the need to standardize those metrics. There is also a need to investigate how occupancy may be contributing to the increase in electricity consumption in newer schools. This study is the first to provide empirical evidence on existing school buildings’ energy consumption in Manitoba, establishing benchmarks that practitioners can make use of in similar cold climates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".