Affordable cost-in-use and neighborhood renewal through energy efficient housing renovations
Bibliographic record
Abstract
Houses undergoing major renovations in Winnipeg's inner city offer an opportunity to minimize cost-in-use by undertaking energy efficient upgrades. To find the optimum point of upgrades, and to choose priorities, a system of energy use analysis and upgrade evaluation was required. To choose and develop an analysis and evaluation model a house undergoing a full-scale renovation in the West Broadway neighborhood was selected as a test case. EnerGuide for Houses is considered to be the most effective system available for identifying areas of energy inefficiency and estimating their contribution to excess energy spending. A financial model is developed to evaluate the target upgrades for their potential contribution to decreasing cost-in-use. Some of the targeted upgrades are found to be financially beneficial, meaning energy savings would exceed the upgrade costs, had they had been done during the renovation. The EnerGuide Evaluation uncovered a costly oversight in not remedying overall building air leakage thereby demonstrating that to maximize the efforts of renovations community groups need to utilize EnerGuide testing prior to undertaking renovations. Further policy recommendations are included at the end of the document. In order to fully take advantage of current, and proposed, information and programs a systematic and coordinated approach is needed to monitor and evaluate renovation procedures.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| 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".