Bibliographic record
Abstract
2021 3rd International Conference on Environment Sciences and Renewable Energy (ESRE 2021) was held as virtual conference during June 5-7, 2021. Conference became a unique platform for leading scientists, teachers, experts and practitioners studying environment sciences and renewable energy. Conference participants were offered with numerous opportunities to present results of their breakthrough research, exchange knowledge and experiences and discuss current problems and trends. The conference is an international conference for the presentation of technological advances and research results in the fields of environment sciences and renewable energy. Its model was divided into two major parts, including keynote speeches and oral presentations, which has brought together more than 60 leading researchers, engineers and scientists in the domain of interest from Malaysia, Russia, Indonesia, India, Peru, Croatia, France, UAE, Philippines, Ethiopia, México, Portugal, Australia, Canada, China, Thailand, Italy and so on. Four distinguished experts in total have given their 45 minutes’ speech as keynote speakers for the conference. They are Prof. Chi Yung Chung from University of Saskatchewan, Canada; Prof. Khaled M. Bali from University of California, San Diego, USA; Prof. Kaimin Shih from University of Hong Kong, China and Prof. Mohamed Benbouzid from University of Brest, France. Their insightful speeches had triggered heated discussion during keynote speech session of the conference. In addition, there are 6 presentation sessions. The topics are Green Technology and Sustainable Development, Waste Management and Recycling Utilization, Environmental Pollution and Control, Wastewater Treatment and Water Resources Management, Agro-Environmental Science and Geochemistry, Food Science and Agricultural Engineering. Each presenter was allocated 12 minutes to deliver speech and 3 minutes for Q&A one by one. Only one presentation has been selected as the best one for each session. All papers presented at the 2021 3rd International Conference on Environment Sciences and Renewable Energy (ESRE 2021) are included in this volume. All the papers have been through peer-review by conference committee members and international reviewers and process to meet the requirements of international publication standard. We would like to acknowledge all of those who have supported ESRE 2021. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the organizing committee for their valuable advices in the organization and helpful peer review of the papers. Prof. Chi Yung Chung University of Saskatchewan, Canada June 23, 2021 Committees are available in the pdf
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.557 | 0.407 |
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".