Bibliographic record
Abstract
Abstract The 6th Asian Conference on Environmental, Industrial and Energy Engineering (EI2E 2022) in conjunction with the 2022 6th International Conference on Environmental and Energy Engineering (IC3E 2022) were co-organized by the Asia Pacific Institute of Science and Engineering (APISE) and the International Society for Environmental Information Sciences (ISEIS). Due to recent pandemic, it was finally held as an online conference via Tecent Meeting Software on May 20, 2022. The decision to hold the virtual conference was made in compliance with many restrictions that were imposed by countries around the world. These restrictions were made to minimize the risk of spreading the COVID-19. Some people suggest to postpone the conference, however, we prefer to use online meeting because of reasons:(1) Most of the authors want to publish their articles as scheduled, as some of them need it before graduation;(2) We don’t know when it will be good enough to hold the conference as usual. The online conference was a great success. The participants were from a number of countries (Canada, India, Chile, Saudi Arabia, Germany, Uzbekistan, China, etc.) with diverse backgrounds, forming a coherent environment for facilitating tremendous learning, sharing and collaborating opportunities Energy and environment are closely interrelated. Energy development is associated with many environmental concerns, where effective approaches for emission minimization are desired. At the same time, a number of innovative technologies for mitigating pollutant/carbon emissions and managing the related risks are being developed. EI2E 2022 is targeted on providing opportunities to bring together the related researchers to share their most recent research achievements in these fields, and thus promote more advanced research. On the behalf of the conference organizing committee, I would like to express my great appreciation to the three keynote speakers (40 minutes each, including Q&A). In addition, the conference included two oral and one poster sessions. In the oral sessions, each presentation was allotted 15 minutes. At the end of each session, the participants were engaged in discussions for future collaborations. A group photo was taken at the conference. List of Conference Chair, Committees, Conference Co-Chairs are available in this 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 | 0.001 | 0.009 |
| 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) | 0.628 | 0.467 |
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".