Strategies for Energy and Climate Management at the British Columbia Institute of Technology
Bibliographic record
Abstract
The British Columbia Institute of Technology (BCIT) is Canada's premier polytechnic. In 2008, BCIT partnered with its local electricity utility to hire a full-time energy manager. The following year, BCIT's School of Construction and the Environment initiated a campus-as-living-lab of sustainability project called Factor Four in the seven buildings it occupies on BCIT's main campus in Burnaby. The purpose was to explore whether a four-fold (75%) reduction in materials and energy use could be achieved without compromising service levels. By 2016, the project achieved a 50% reduction in energy use and associated greenhouse gas emissions. Factor Four attracted over four million dollars in funding, engaged over 250 students from 12 educational programs, and produced over $200,000 savings annually. In 2017, BCIT set an ambitious target to reduce its annual greenhouse gas emissions 33% below 2007 levels by 2023, and 80% by 2050, across all five of its campuses. BCIT’s ultimate goal is to become both greenhouse gas neutral and a net energy producer. By setting ambitious targets and systematically implementing energy efficiency improvements, utilizing waste-heat exchange, fuel switching, and developing on-site renewable energy, BCIT is on track to achieving its energy management and climate change goals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".