Upgrading Existing Housing in Yellowknife to Achieve Energy Efficient Standards
Bibliographic record
Abstract
Across Canada, building codes are becoming increasingly stringent for new construction. There are plans for all provinces and territories to build new buildings to net-zero energy by 2030. However, the existing building stock makes up most of the buildings in Canada and this study explores an existing house in one of Canada’s most extreme climates, in Yellowknife. Three targets are aimed to be achieved: The City of Yellowknife new build requirement, EnerPHit equivalent for an Arctic Climate and Net-Zero Energy. A single-family detached home, including typical construction for pre-1975, in Yellowknife was analyzed to determine if achieving a net-zero energy building using on-site renewable energy is possible. An envelope-first approach was taken to then improve the mechanical and electric loads. Ultimately, the City of Yellowknife target of 105 kWh/m2/year for TEDI was achieved but the EnerPHit and Net-Zero Energy Targets were not. For existing buildings exposed to extreme climates, it will require more than upgrades to the existing building infrastructure to achieve such targets. However, with the use of renewable energy technology, the building EUI and TEDI were reduced to 13.94 kWh/m2/year and 0 kWh/m2/year.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".