Bibliographic record
Abstract
It is our great pleasure to welcome you to 2019 10th International Conference on Environmental Science and Technology (ICEST 2019) which was successfully held in Xiamen Ocean Vocational College, Xiamen, China during June 7-9, 2019.ICEST 2019 is co-organized by Xiamen Ocean Vocational College and Xiamen University of Technology, assisted by International Society for Environmental Information Sciences (ISEIS), Hong Kong Chemical, Biological & Environmental Engineering Society (HKCBEES), Environment and Agriculture Society (EAS), Fujian Smart City Association. ICEST 2019 is dedicated to issues related to environmental science and technology. 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. Changping Chen from Xiamen Ocean Vocational College, China gave the opening remark; Prof. Caterina Valeo from University of Victoria, Canada gives her keynote speech” Scaling in Sustainable Urban Design”; Prof. Yongping Li from Xiamen University of Technology, China gives her keynote speech “Water-energy-food nexus: Integrated simulation-optimization approach”; Prof. Lixiao Zhang form School of Environment, Beijing Normal University gives his keynote speech” Food-Energy-Water Nexus for Urban Sustainability: Conceptual Framework and Real Challenges”; Prof. Guangwei Huang from Sophia University, Tokyo, Japan gives his keynote speech” Shallow Lake Restoration: Case Studies in Japan”; Prof. R. J. (Dick) Haynes from The University of Queensland, St Lucia, Queensland gives his keynote sppech” Sustainable revegetation of bauxite processing residue”; Prof. Gordon Huang from Faculty of Engineering and Applied Science, University of Regina, Canada gives his keynote speech “Management of Watershed Environmental Risks”. Many researchers, engineers, academicians as well as industrial professionals from all over the world have presented their research results and development activities. There were five sessions: Modeling of Environmental Systems, Environmental Pollution Control, Environmental Management and Planning, Workshop on Environment, Water and Energy, Forum on South-South Sustainable Development. 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.
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 | 0.002 | 0.011 |
| 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.532 | 0.365 |
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".