Bibliographic record
Abstract
Abstract • Introduction of the meeting 2022 3rd International Conference on Materials, Physics and Computers (MPC 2022) & 2022 3rd IETI Materials and Engineering Forum, it will be held on July 30-31 2022 in Kuala Lumpur, Malaysia (online conference by ZOOM, due to COVID-19/travel restrictions, etc). The MEF is the annual conference of the International Water, Air and Soil Conservation (INWASCON) Society and International Engineering and Technology Institute (IETI). It is organized by International Water, Air and Soil Conservation (INWASCON) Society, International Engineering and Technology Institute, co-organized by NanoFemto Lab, Canada, Center for Advanced Diffusion-Wave and Photoacoustic Technologies (CADIPT), Department of Mechanical and Industrial Engineering, University of Toronto, Canada, Hohai University Institute of Physics, China, Interactions, Dynamics, and Energetics in the Atmosphere (IDEA) Team, The Pennsylvania State University, USA, Integrated Energy Solutions for Entrepreneurs (IESE) Program, The Pennsylvania State University, USA, Wuhan University of Technology Nano Key Lab, China, Department of Mechanical Engineering, Northern Illinois University, USA. he conference received a record 48 submissions, with 23 papers accepted for presentation. Positive recommendations of at least two reviewers were considered by the conference committees for acceptance of manuscripts. List of Tributes to colleagues, Sponsor acknowledgements, MPC 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.545 | 0.413 |
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".