Stimulating The Use Of Renewable Energy In The Canadian Residential Sector With Economic Instruments
Bibliographic record
Abstract
Abstract Renewable energy offers the promise of electricity and heat with significantly reduced environmental impacts compared to conventional energy sources. Efforts by the Canadian federal and provincial governments have played an important role in helping to establish a market for large-scale renewable projects, but markets for mediumand small-scale projects, such as those that would be used by homeowners, have not developed as quickly. Existing homeowners and new home buyers today are increasingly interested in considering ‘green’ forms of energy for their nest, whether through renovations or new construction. They are motivated by a variety of factors, including the potential for long-term savings and reducing their energy footprint. Renewable energy technologies can help to meet household energy needs with reduced greenhouse gas emissions and local air pollution. Increasing distributed power sources also boosts the robustness of the country’s energy networks, not only reducing demand for centralized supply, but also reducing losses and costs associated with shipping or transmitting energy from its source to its end use. The possibility of realizing these benefi ts through increased deployment of renewable technologies, however, depends largely on where people live. While production and use of renewable energy is on the increase around the world, the availability and cost of technologies varies widely.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".