Bibliographic record
Abstract
Abstract 2021 3rd International Conference on Computer, Communications and Mechatronics Engineering (CCME2021) was successfully held on December 17, 2021 in the form of Virtual Conference because of current epidemic prevention and travel restriction, which is a professional academic platform for all the participants. While improving the research motivation and academic literacy of all participants, CCME2021 will more strengthen safety protection, actively respond to the current epidemic prevention and cooperate with the normalization of epidemic situation. CCME2021 virtual meeting mainly carries out academic sharing and exchanging in the forms of Opening ceremony, Keynote Speech, Oral presentations, Poster presentations and free discussions. In order to ensure this virtual meeting smooth running, CCME2021 organizing committee specially opened a test room before opening for experts and authors to test their participation conditions including video, audio and screen sharing, etc. in advance. Thank you for all supports of Prof. Shuo Zhao, Dr. Hamed Taherdoost and all attendees. Prof. Shuo Zhao from Communication University of China, China made keynote speech about Corpus-involved E-learning and Education in European Universities, and Dr. Hamed Taherdoost from University Canada West, Canada shared his viewpoints on The Role of Information Security Awareness In Online Shopping Trends. List of Committees, General Chairs, General Co-Chairs, Publication Chairs 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 | 0.009 |
| 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) | 0.556 | 0.389 |
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".