Smart Generation : powering Ontario with renewable energy
Bibliographic record
Abstract
This report describes how Ontario can develop renewable energy sources to replace fossil fuels currently used for heating and cooling homes. A switch to renewable energy to power the electricity system would promote energy efficiency and conservation and would add $9 billion to the Ontario economy by 2010. An added benefit would be a more reliable electricity system and cleaner air. The economic benefits of the 5 main sources of renewable energy were discussed. These included wind, hydropower, biomass, geothermal and solar energy. Specific policy recommendations for rebuilding Ontario's electricity system with these renewable energy sources were presented. The report showed how Ontario could install 8,000 MW of wind power by 2012 and generate 9 per cent of current electricity demand. Farmers view wind energy as a new cash crop because they can earn thousands of dollars per year by installing wind turbines on their farms. The Ontario government has responded to public concerns about air pollution by promising to close down five coal-fired power plants by 2007. The closures will result in an imbalance between electricity supply and demand. The imbalance of about 7,500 MW can be filled with cheaper and more reliable renewable energy. Canada's first full-scale solar manufacturing plant was built in Cambridge, Ontario and was operational in June 2004. The report suggests that Ontario can install more than 12,000 MW of renewable energy by 2020, enough to phase out coal plants in Ontario. The economic benefits of installing 8,000 MW of wind energy are in the order of $14 billion. refs., tabs., figs.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".