Bibliographic record
Abstract
Abstract – A recent study by the Photovoltaics and Hybrid Systems Program at CANMET Energy Technology Centre-Varennes examined the benefits of on-site generation of electricity using grid-tied photovoltaic technology on buildings in Canada. The study focused on the grid-connect segment of the PV market, which has been experiencing the strongest growth. It showed that while costs of PV worldwide have been falling at about 5 % per year in real terms over the past twenty years following a well-established learning curve, there remain several barriers to be addressed before greater inroads can be made in this sector of photovoltaics in Canada. There are many challenges to grid-tied PV in Canada. CETC-Varennes has been addressing these challenges through their RD&D programactivities. These activities range from removing interconnection barriers to the grid, providing assistance to Canadian industry by championing climate change TEAM projects, accelerating the development of adequate policies, and providing quality information to Canadians. The outcomes of these efforts are elucidated in this paper. CETC-Varennes is committed to continuing building alliances with the private sector and with other federal, provincial and municipal levels of government to mainstream grid-tied PV in Canada. It will continue to seek out technology investment opportunities, such as those provided by TEAM, in order to share the RD&D risk and to demonstrate the benefits of linking private sector
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.697 | 0.414 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".