Bibliographic record
Abstract
We are pleased to provide you the proceedings of 2021 3rd International Conference on Resources and Environment Sciences (ICRES 2021). ICRES 2021 was held between June 5-7, 2021 in Kasetsart University, Bangkok, Thailand. Most countries still have travel restrictions due to COVID-19 pandemic around the world. The situation of COVID-19 pandemic is unpredictable and unstable. Most of countries are still fighting the COVID-19 pandemic, conference participants could not travel to attend the conference venue to do oral presentations. Therefore, ICRES 2021 was held online during June 5-7, 2021. The location of organizers is Building 38, No.1 Shilibao, Chaoyang District, Beijing, China. We used ZOOM application which made the virtual format conference come true. The conference was highlighted by four outstanding Keynote Speakers: Prof. Chi Yung Chung from University of Saskatchewan, Canada who presented a talk on “Advanced Prediction Techniques Applied to Smart Grids” Prof. Khaled M. Bali from University of California, Dvision of Agriculture and Natural Resources, USA who presented a talk on “Sustainability of Water Resources in California Under Scarcity” Prof. Kaimin Shih from the University of Hong Kong, China who presented a talk on “Persistent Perfluorochemical Pollutants in the Environment and Thermal Treatment Processes” Prof. Mohamed Benbouzid from University of Brest, France who presented a talk on “Tidal Stream Turbine Systems Resilient Control: The key to Improve Availability and Decrease Maintenance Costs”. Each oral presenter had about 12 Minutes of Presentation and 3 Minutes of Question and Answer. Conference was organized in 6 sessions with various topics: 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. All accepted papers presented at the ICRES 2021 were included in this volume, which contained three chapters with topics: (1) Environmental Science and Sustainable Development (2) Resource Management and Waste Utilization (3) Geology and Geochemistry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on high quality and high relevancy to the conference scope. ICRES 2021 virtual conference provided an ideal platform for participants to share views and experiences in Resources and Environment Sciences and related areas. We would like to express our sincere gratitude to organizing committee and the volunteers who have dedicated their time and efforts in planning, promoting, and helping the conference. We hope that the readers would gain some valuable knowledge from this effort. We are expecting more and more experts and scholars from all over the world to join this international event next year. Prof. Khaled M. Bali, Statewide Irrigation Water Management Specialist University of California, Division of Agriculture and Natural Resources, USA July 19, 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 | 0.017 |
| 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 | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.559 | 0.428 |
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".