Bibliographic record
Abstract
Abstract It was our great pleasure to welcome all presenters/authors to the 5th Asian Conference on Environmental, Industrial and Energy Engineering (EI2E 2021). EI2E 2021 was organized by the Asia Pacific Institute of Science and Engineering (APISE). Due to the COVID-19 pandemic, the local government issues many limitations on domestic and international travelling. Thus most of the authors chose the online presentation mode. In consideration of the authors’ safety, to enable more authors to participate, and to reach a wider audience for the conference, EI2E 2021 was finally held as a hybrid conference via Tecent Meeting Software during May 20 to 22, 2021. EI2E 2021 is the international conferences for presenting novel research progress in the fields of Energy and Environmental research. It also serves to foster communication among researchers and practitioners working in a wide variety of areas with a common interest in improving environment protection. The conference enabled many researchers, engineers, and practioners from all over the world to present their most recent achievements. The participants are from Thailand, Canada, Saudi Arabia, Indonesia, Morocco, Peru, China, etc. The conference featured 3 Keynote Speakers (40 minutes each, including Q&A). In addition, the program included 2 oral sessions and 1 poster session. The papers in the oral sessions were allotted 12 minutes each for presentation. Following the end of each session, the audience engaged in technical dialogues and discussed possible ways for future collaboration. A group photo was taken at the conference. List of Committees are available in this 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.535 | 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".