Bibliographic record
Abstract
AMTEC and Universiti Teknologi Malaysia (UTM) successfully held a two-day joint online event on 16-17 January 2021 with the involvement of the Regional Conference on Environmental Engineering (RCEnvE2020) and the Regional Congress on Membrane Technology 2020 (RCOM2020) under the theme ‘Enhancing Translational Research into Sustainable Environmental Technology.’ RCOM 2020 & RCEnvE 2020 is a premier forum for the presentation of new advances and research results in the field of membrane and environment. The conference has brought together networks of professionals and researchers worldwide in various disciplines to discuss current technology development in different field and valuable information of the latest research activities in maintaining the sustainability of the environment for the future. All papers were presented on-line on Webex platform in about 15 minutes plus 5 minutes for Q&A, managed by the Universiti Teknologi Malaysia, in Johor Bahru, Malaysia, divided in 5 parallel sessions. In addition, four plenary speeches on interesting issues in membrane manufacturing and environmental engineering inspired the participants and provided a platform for valuable discussions. More specifically, the plenary sessions were the following: Professor Dr Ishihara Keiichi from the Kyoto University, Japan speaking on greenhouse gases mitigation by agrivoltaic technology, Prof Dr Bunsho Ohtani from Hokkaido University, with a lecture on novel approach on efficient photocatalyst design, Professor Emeritus Dr Takeshi Matsuura from University of Ottawa, Canada talking about recent progress on membrane transport theory and design and last but not least, Dato’ Norhalim Yunus from Malaysian Technology Development Corporation with a speech on the enhancing translational research towards sustainable environmental technologies. List of Topics, 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 | 0.011 |
| 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) | 0.532 | 0.351 |
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".