1 | Feed-In Tariffs Are Awesome www.ilsr.org
Bibliographic record
Abstract
Launched in 2009, Ontario’s “buy local ” Feed-In Tariff (FIT) program promised to deliver hundreds of megawatts of new renewable energy and create 50,000 new jobs by the end of 2012. The program has had some notable achievements, and the province has worked hard to remedy some of the remaining roadblocks to success. The bottom line is that the FIT program and its predecessors (despite facing significant threats) have jumpstarted renewable energy development in Ontario: the province would rank #4 and #11 for solar and wind deployment, respectively, if it were a U.S. state. It has created 31,000 jobs. It has also enabled widespread participation in renewable energy generation: 1 in 7 Ontario farmers is participating, earning a return on their investment. Finally, it has enabled the province to shut down all of its coal-fired power plants by the end of 2014. Huge Interest The biggest challenge for the FIT program is the overwhelming demand. Already, signed contracts for nearly 5,000 megawatts of new renewable energy capacity will allow the province to meet most of its 2030 renewable energy target, 12 years early. Actual deployment has kept pace with many U.S. states, but poor preparation has meant that less than 10 % of energy under contract (thus far) is actually producing electricity. Success with Small The MicroFIT program (mostly 10 kilowatt and smaller solar) has been a huge success. More than half of the 230 megawatts of solar added to the grid under the FIT program has been from the MicroFIT program, serving almost 15,000 individuals and small businesses.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.354 | 0.151 |
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".