RETScreen{sup R} International : results and impacts 1996-2012
Bibliographic record
Abstract
The notable achievements that the RETScreen International Clean Energy Project Analysis Software has earned since its launch in 1996 were presented along with an independent study that assesses the present and future impacts of this decision support tool which evaluates the economics of renewable energy installations. RETScreen is focused on overcoming the barriers for implementing renewable energy technologies (RETs) across Canada and building a foundation for sustainable development. The analysis tool has been used in 196 countries (mostly industrialized countries) and has been integrated into other enabling tools such as international product cost and weather databases and and online user manuals. Its use can help reduce the cost of pre-feasibility studies. The primary distribution and communication point for RETScreen International is a website where users can access all products and services available, including an electronic textbook for professionals and university students interested in learning how to analyse the technical and financial viability of clean energy projects. A list of 20 Canadian and 20 international projects facilitated by RETScreen was included along with a work plan for 2004 to 2008. To date, 616 projects have been launched using RETScreen. These projects have a cumulative installed capacity of 1,150 MW with an average reported annual savings of $34,257 per user for project implementers and $7,872 per user for project facilitators. refs., tabs., figs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".