Bibliographic record
Abstract
We are honored to present this special issue on Green Energy in the International Journal of Energy Research. The issue consists of nine original, peer-reviewed articles, which have been presented at the Fifth International Green Energy Conference (IGEC-V), held in Waterloo, Ontario, Canada, on 1–3 June 2010. The papers presented in this special issue have been accepted according to the established IJER standard after the established peer review process. The International Green Energy Conference (IGEC) is a multi-disciplinary international conference, aiming to bring researchers together to advance research and technological innovation/development pertaining to all aspect of energy for humanity without undue impact on the environment and social and economic development through energy diversity, energy localization, energy security, and energy sustainability. IGEC-V was organized by the International Association for Green Energy with partnership with the University of Waterloo. This was the second time for the University of Waterloo to host the conference. The first IGEC was hosted at the University of Waterloo in 2005; the second IGEC was in Oshawa, Canada, in 2006; the third IGEC in Västerås, Sweden, in 2007; and the fourth was in Beijing, China, in 2008. The IGEC-V covers a wide range of topics in green energy area. Some of the topics covered are and not limited to the following: energy analysis/management, solar energy, wind energy, hydrogen production/storage, fuel cells, biofuels, green buildings, and emission abatement. These topics offered the opportunity for many researchers, students, scientists, engineers, educators, government officials, consultants, managers, and others from across the world to share their findings and research and have significant discussions pertaining to these topics. The conference featured seven keynote speakers, three specialized sessions, three panel sessions, and 17 parallel sessions. Furthermore, this conference offered a half day networking session, where many government and industry leaders were invited to discuss crucial topics on green energy research and its integration into society. We wish to express our deepest appreciation to the organizing committee and volunteers who have made IGEC-V an unforgettable experience. We also would like to extend our sincere thanks to all the authors and reviewers of this special issue and the editor of IJER who made this special issue a reality.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.058 | 0.034 |
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".