Bibliographic record
Abstract
More than 60% of household energy consumption in Ontario is for heating. Home heating needs in Ontario are driven by exterior temperatures that fluctuate throughout the day. Ontario’s electricity is generated from a different mix of primary energy sources from hour to hour. Using average hourly data for the electricity generation mix and hourly outside temperature data for each month of the year, we estimate residential heating loads and the electricity demands due to the use of three models of heat pump. Then we calculate the resultant greenhouse gas emissions and compare them to emissions if heat pumps are not used. We determine heating needs of single detached dwellings using prototypical average Ontario homes and building simulation software. Using heat pumps in all of these dwellings can reduce heating-related greenhouse gas emissions between 15% and 85% during January, the harshest month of the year. Using heat pumps could also reduce energy consumption for heating by between 12% and 68%, while requiring an approximate 5–25% increase in electricity demand. Heat pumps can provide a significant portion of home heat needs whilst reducing energy consumption and greenhouse gas emissions. Operating costs are lower than that of electric and oil heating, but similar to natural gas heating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".