Bibliographic record
Abstract
The 6th International Conference on Eco-Engineering Development (The 6th ICEED 2022) was held virtually on November 16–17, 2022, hosted by the Faculty of Engineering, Bina Nusantara University, Indonesia. The 6th ICEED, specifically, aims to bring together researchers, engineers, scientists, and experts to not only share their ideas but also disseminate their knowledge and research on achieving comprehensive and immaculate eco-engineering. ICEED 2022 would focus on the research, analysis, and resolution of environmental development through innovative technology, green infrastructure, planning, and design, delivered by the keynote speakers and distinguished lecturers. The ICEED 2022 offered three scopes of interests, including sustainable infrastructure management and technology, eco-architecture planning and design, and innovative food technology. From all these scopes, ICEED 2022 received 225 submitted research papers. Through the double-blind peer-review process, the committee carefully selected 128 research papers that were presented at the conference. This conference is also very special because our keynote speakers are prominent scholars and professionals from Taiwan (Prof. Dr. Chin-Kun Wang), Canada (Assoc. Prof. Dr. Agus Pulung Sasmito), Brunei Darussalam (Assoc. Prof. SMN Arosha Senanayake, PhD), Saudi Arabia (Ardian Nengkoda, Ph.D.), and Indonesia (Prof. Dr. Eng. Made Suangga). They discussed cutting-edge ideas in eco-engineering from the perspectives of academics, professionals, and subject matter experts in various fields. Finally, I would like to convey my appreciation to the conference organizer, the technical program committee, and the reviewer. We also thank all the authors for their outstanding work in making the conference a success and worthwhile endeavor. We also want to express our sincere gratitude to the editors and managers at IOP Publishing for their supportive collaboration during the preparation of the proceedings. List of Organizing Committee is 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 | 0.002 | 0.010 |
| 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 | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.536 | 0.379 |
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".