Proven Delivery Models for LED Public Lighting : Joint Procurement Delivery Model - Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The context in Ontario was encouraging \n for light-emitting diode (LED) programs at federal, \n provincial, and municipal levels. At the federal level, \n there were a number of incentive programs funded by the \n federal gas tax that municipalities tapped into for \n municipal energy efficiency retrofits and upgrades. The \n federal gas tax and a yearly transfer from the Government of \n Canada to each municipality based on population; it is an \n environmental measure aimed at reducing greenhouse gases. \n Independent electricity system operator (IESO) has a \n comprehensive master plan with ambitious energy efficiency \n goals, implemented with both environmental and economic \n rationales in mind. IESO’s conservation first framework, \n developed in 2014, maps out Ontario’s energy conservation \n goals from 2014 to 2020, emphasizing a coordinated effort \n within all stages of energy planning, as well as more \n effective teamwork among sector partners, particularly in \n support of local distribution companies (LDCs). The global \n adjustment mechanism fund covers various initiatives, \n including the province’s energy conservation and demand \n management programs. As part of this commitment to energy \n conservation, IESO provides very significant fiscal \n incentives of up to 30 percent for energy efficiency in the \n form of energy efficiency infrastructure rebates through a \n program called save on energy. It is possible that IESO will \n lower the amount of the outdoor lighting incentive, as it is \n known, to reflect the growing cost-competitiveness of LEDs \n in the market without incentives.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it