Bibliographic record
Abstract
On account of the serious situation of COVID-19, the 2020 2nd International Conference on Resources and Environmental Research (ICRER 2020) was held as virtual conference during November 19-21, 2020. Travel restrictions were made to minimize the risk of people spreading the COVID-19 through physical contact. Therefore, conference committees decided to hold ICRER 2020 by Zoom application and it aims to provide a platform for Resources and Environmental Research fields to share views and experiences. ICRER 2020 is co-organized by Xiamen University of Technology, supported by South-South Collaborative and Sustainable Development Center and International Society for Environmental Information Sciences (ISEIS). This three-day conference focused on the research fields in Resources and Environmental Research. Environmental Resources refer to the environment as a whole as the sum of resources. All kinds of natural resources, including water, air, land, animals and plants, minerals and their combination of various states, are the material basis for human survival and development. Irrational development and utilization of environmental resources have led to increasingly serious damage, such as water resource shortage and pollution, ozone layer destruction, soil desertification, forest area reduction and the imminent exhaustion of some rare species and minerals, which will have a corresponding impact on human social progress and economic development. The conference is an international conference for the presentation of technological advances and research results in the fields of Resources and Environmental Research. The conference has brought together more than 70 leading researchers, engineers and scientists in the domain of interest from Indonesia, Portugal, Australia, Canada, Japan, Romania, China, Thailand, Italy, Argentina, UK and so on. Six distinguished experts in total have given their 35 minutes’ speech as keynote speakers for the conference. They are Prof. Yongping Li from Beijing Normal University, China; Assoc. Prof. Farhad Shahnia from Murdoch University, Australia; Prof. Guilin Zheng from Wuhan University, China; Prof. Wei-Jen Lee from University of Texas at Arlington, USA; Assoc. Prof. S. M. Muyeen from Curtin University, Australia; Prof. Zhijun Peng from University of Bedfordshire, UK. Their insightful speeches had triggered heated discussion during keynote speech session of the conference. In addition, 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. List of Committees are available in the pdf.
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 | 0.001 | 0.008 |
| 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 | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.541 | 0.382 |
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".