The statistic energy profile analysis and carbon renovation plan of the household energy use : taking the Region of Waterloo as an example
Bibliographic record
Abstract
As one of the largest and fastest growing urban areas in Ontario, the Regional Municipality of Waterloo is serving a continuously growth of population, as well as an increasing demand for energy input. Since the residential sector always take up a significant amount of energy consumption, and generate an increasing amount of Green House Gas emissions, solutions of reduce energy consumption and Green House Gas emissions are highly demanded. \nThe paper will start with an introduction of the overall statistics on national and regional energy consumptions. Participating households from the regional Waterloo REEP program were analyzed and evaluated for their energy consumption status and energy efficiency performances to draft an energy profile will be explained in the next section, since the performance-based profile will be able to provide both area municipalities and residents with better understanding of how the dwellings consumes energy and empower them to come up with smarter energy adjustments at home. The follow-up improvements and renovation plans will be initiated based on the energy profile, including examining the wind energy potential in Waterloo region and households retrofitting designs will be illustrated at last.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".