Preface of ICSEE 2020 Proceedings
Bibliographic record
Abstract
Welcome to the Proceedings of the 2020 4th International Conference on Sustainable Energy Engineering (ICSEE 2020), which was held during May 23-25, 2020. The conference is now in its 4th edition and has been gaining increasing visibility and extensive international participation. In the past decades we have witnessed incredible growth of renewable and sustainable energy. The growing fields have moved past experimentation toward realization and plays an important role in economic development and environmental protection. Sustainable energy has been the technological development of diverse ways from improving energy efficiency to new energy, to support deployment, effectively removing time and place constraints. This provides opportunities to foster engineers, researchers and students’ active involvement. Despite many successes, there is still a wide need for research and development in this field, because the technological and cultural contexts require new paradigms for measuring energy sustainability, pragmatic approaches for applying renewable resources and efficiency improvements, and unique insights on managing risk in heat and power production facilities. This conference aims to offer vital information for the universities, corporations, and engineering firms that must apply exciting new energy technology in the real world. In this respect, the conference brings together, engineers, researchers, students, professionals and policy makers who are interested in sustainable energy engineering. Its focus includes all facets of the field, ranging from theoretical aspects to technological issues, and up to the design and implementation of sustainable energy engineering. On the behalf of organizing committees, I would like to express my great appreciation to Prof. C.Y. Chung, from the University of Saskatchewan (Canada), Prof. C.K. Michael Tse, from the City University of Hong Kong (Hong Kong), and Prof. Junye Wang, from Athabasca University (Canada) for accepting to be keynote speakers at ICSEE 2020. I would also like to thank the Program Chairs, the Session Chairs, the members of Advisory and Technical Committee and the reviewers who carefully evaluated the submissions and provided useful suggestions to improve them. Our thanks go to the authors with diversity of their backgrounds, experiences, interests, and perspectives for submitting their papers, presenting them at the conference and getting involved in discussions. Finally yet importantly, I would thank the conference organizers for taking care so effectively of all the logistic and bureaucratic aspects of the conference. This make this conference successful in rewarding, highlighting, and intertwining the richness of experiences and applications in diverse environments. I hope that all participants had an enjoyable and productive time. I also hope that all the readers of the proceedings will find in them useful in inspiring hints, supporting their work in Sustainable Energy Engineering, and by this means finally contributing to further promote the field. Conference Program Chair of ICSEE 2020 Prof. Junye Wang, Athabasca University, Canada May 30, 2020
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.323 | 0.231 |
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".