Bibliographic record
Abstract
Abstract The 5th International Conference on Environmental and Energy Engineering (IC3E 2021) was held in Yangzhou, China on March 19 to 21, 2021 as a combination of on-line and offline event due to the growing concerns over the coronavirus outbreak (COVID-19), and in order to protect the well-being of our attendees, partners, and staff as our number one priority. IC3E 2021 was organized by Asia Pacific Institute of Science and Engineering (APISE). This conference aimed to provide a platform for researchers from multiple disciplines to share their recent developments and practices in Environmental and Energy Engineering. The participants of the conference were from many parts of the world. The success and prosperity of the conference were reflected through a number of high-quality papers. This scientific event brings together more than 74 national and international researchers in Environmental and Energy Engineering. On top of the local participants coming from different national universities, international participants are also registered from different countries, namely Thailand, India, Nigeria, China, Canada, USA and so on. During the conference, there are keynote speeches, oral presentations, and poster presentations. The proceedings form a compilation of accepted papers for this conference. It includes 3 chapters, covering the areas of Energy and Power Systems Engineering, Environmental and Ecological Systems Engineering, and Advances in Energy and Environmental Studies. 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 | 0.002 | 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) | 0.547 | 0.381 |
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".