Bibliographic record
Abstract
2022 6th International Conference on Frontiers of Sensors Technologies (ICFST 2022) was sponsored by Jiangsu University of Science and Technology, China and technical supported by University of Chinese Academy of Sciences, China. Due to the ongoing anti-epidemic measures, the conference was finally held online via Zoom during November 18-20, 2022. Featured invited keynote speeches as well as peer-reviewed paper presentations, ICFST 2022 provided a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Frontiers of Sensors Technologies. It is with great pleasure that I present a printed version of the papers given at the conference. This year, 11 papers were accepted to be collected in the conference proceedings-Journal of Physics: Conference Series. All the contributed papers were rigorously peer-reviewed by international reviewers who are organizing and advisory committee members as well as experts in the field from all over the world. On behalf of the organizing committee, I would like to express my sincere gratitude to all the reviewers for their great professionalism and efforts. Finally, the appreciation also goes to all participants for their contributions to the conference program and for their contributions to this Proceedings. The proceedings is divided by topic into 3 chapters: Advanced Sensor Principle and Design, Sensor Measurement and Application and Material Performance Analysis and Preparation It is my hope that you will find that the contributions presented in this proceedings are valuable references for supporting the development in the fields of Frontiers of Sensors Technologies in future. Hope to see you next year onsite! Conference Chair Prof. Cheng Li, Memorial University of Newfoundland, Canada List of Committees, Statement of Peer Review are 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 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.515 | 0.405 |
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".