Bibliographic record
Abstract
It is our great pleasure to welcome you to 2019 International Conference on Resources and Environmental Research (ICRER 2019) which was successfully held in Shandong University, Qingdao, China during October 25-27, 2019. ICRER 2019 is co-organized by Shandong University, assisted by Xiamen University of Technology, South-South Collaborative and Sustainable Development Center, International Society for Environmental Information Sciences (ISEIS) and Shandong University of Technology, Hong Kong Chemical, Biological & Environmental Engineering Society (HKCBEES), Environment and Agriculture Society (EAS). ICRER 2019 is dedicated to issue related to resources and environmental research. The major goal and feature of the conference is to bring academic scientists, engineers, industry researchers together to exchange and share their experiences and research results, and discuss the practical challenges encountered and the solutions adopted. Prof. Shuguang Wang from School of Environment Science and Engineering, Shandong University, China has been invited to do the welcome address; Prof. Edward McBean from University of Guelph, Canada has shared his keynote speech” Challenges for the Future, for Sponge City”; Prof. Yongping Li from Beijing Normal University, China has presented her keynote speech “Sustainable water resources management under uncertainty – A case study of Central Asia”; Prof.Zhijun Peng from University of Bedfordshire, UK has given his keynote speech” The Way to Save CO2 Emissions with BEVx (Plug-in Pure Battery Electric Vehicles)”; Prof. Christophe Guimbaud from Université Orléans, France has presented his keynote speech” Impact of global changes on Greenhouse Gas (GHG) exchanges with atmosphere for sphagnum type peatlands: field study and modelling approach for methane emissions”; Dr. Pengfei Xia from Center for Applied Geosciences, University of Tübingen, Tübingen, Germany has given his keynote speech” Harnessing synthetic biology for recycling carbon dioxide”; Prof. Gordon Huang from University of Regina, Canada has shared his keynote speech “The Way to Save CO2 Emissions with BEVx (Plug-in Pure Battery Electric Vehicles). Many researchers, engineers, academicians as well as industrial professionals from all over the world have presented their research results and development activities. There were three topics for all the session presentations: Modeling of energy management systems, Energy and environmental Studies, Technologies of power and energy engineering. It will be a golden opportunity for the students, researchers and engineers to interact with the experts and specialists to get their advice or consultation on technical matters, sales and marketing strategies. This conference proceeding presents a selection from papers submitted to the conference from universities, research institutes and industries. All of the papers were subjected to peer-review by conference committee members and international reviewers. The papers selected depended on their quality and their relevancy to the conference. The volume tends to present to the readers the recent advances in the field of resources and environmental research and various related areas. We would like to thank all the authors who have contributed to this volume and also to the organizing committees, reviewers, speakers, chairpersons, sponsors and all the conference participants for their support to ICRER 2019. Prof. Shuguang Wang School of Environment Science and Engineering, Shandong University, China November 25,2019
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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 | 0.002 | 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) | 0.552 | 0.375 |
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".