Bibliographic record
Abstract
Considering the unpredictable situation of COVID-19, as well as the health and safety of our participants and members of our research community is of top priority to the organizing committee. 2023 3rd International Conference on Electrical, Electronics and Computing Technology (EECT 2023) planned to be held from March 24th-26th, 2023 in Sanya, China was changed to be held virtually through Zoom software. EECT 2023 is dedicated to addressing the challenges in the areas of Electrical, Electronics and Computing Technology as well as its applications, presenting the latest scientific research results related to these topics. EECT 2023 looks forward to bringing together researchers and practitioners from academia and industries to focus on related topics and establishing new collaboration in these areas. The conference was divided into three sessions, including keynote speeches, oral presentations, and poster presentations. In the keynote presentation’s part, we have 3 renowned professors present their insightful speeches. Each speaker allocated 50 minutes including 5min for Q&A to hold their speeches. Prof. Jizhong Zhu from South China University of Technology, Prof. Witod Pedrycz from University of Alberta, Prof. Fushuan Wen from Zhejiang University. For oral presentations, authors were given approximately 10-15 minutes to perform their oral presentations one by one. List of Conference Committee 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.448 | 0.325 |
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".