Guest Editorial Special Section for Third International Conference on Sensing, Measurement, and Data Analytics in the Era of Artificial Intelligence (ICSMD 2022)
Bibliographic record
Abstract
The Third International Conference on Sensing, Measurement, and Data Analytics in the Era of Artificial Intelligence (ICSMD 2022) aimed at providing a dedicated forum for researchers, scientists, engineers, and practitioners throughout the world to present their latest research findings in the area of sensing technology, measurement methodology, and data analytics approaches in the fast-changing era of artificial intelligence. ICSMD 2022 was jointly organized by the Harbin Institute of Technology, the China Instrument and Control Society, the Heilongjiang Instrument and Control Society, the Chinese Institute of Electronics, and the IEEE Instrumentation and Measurement Society Harbin Chapter on December 22–24, 2022, in an online and offline manner. Over 500 attendees from academia and industry joined ICSMD 2022. Three keynote speeches were respectively given by Prof. Shervin Shirohammadi at the University of Ottawa, Prof. Mingjian Zuo at Alberta University, and Dr. Fushun Nian at Ceyear Company Ltd. Over 100 oral presentations and over 70 poster presentations appeared in ICSMD 2022. Researchers, scientists, engineers, and practitioners joined several different sessions and had deep discussions.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.046 | 0.037 |
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".