Bibliographic record
Abstract
Concerns over climate change and other detrimental effects of conventional energy sources have resulted in the introduction of new federal government programs to promote renewable energy technologies (RETs). Two key initiatives of the Department of Natural Resources (NRCan) of the Government of Canada that are designed to further this objective are the RETScreen®International Renewable Energy Decision Support Centre and the Renewable Energy Deployment Initiative (RED!). While the emphasis of RED! is on developing the Canadian market for renewables and providing direct financial incentives for individual RET projects, RETScreen provides the tools and human capacity building to enable the successful implementation of RETs in Canada and internationally. Both programs have shared resources and pooled their strengths to attain their complementary objectives. As a result, they have achieved considerable success in their mandates and offer valuable lessons for Korea and other countries seeking effective models to disseminate renewable energy technologies. Korea is already well on the road of benefiting from this experience: the Korean network of certified RETScreen trainers will be expanded significantly via a training workshop in conjunction with the annual conference of the Korean Solar Energy Society (KSES) on November 26-27, 2003. Also, significant knowledge transfer in regard to RED! and RETScreen program design has already occurred between Canada and Korean organizations such as KSES and the Korean Institute for Energy Research (KIER). This paper is intended to provide Korean readers with an overview of the RETScreen and REDI initiatives and shows how the two interact to help bring about the \implementation of RETs in Canada and internationally, and to offer these experiences as examples for consideration in Korea.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".