Bibliographic record
Abstract
It is our pleasure to welcome you to the 2019 8th International Conference on Mechatronics and Control Engineering (ICMCE 2019) in Paris, France on July 23-25, 2019. ICMCE 2019 is a conference dedicated to topics in acoustics and noise control, computational mechatronics and engineering design management, mechatronics, process control & instrumentation, automated guided vehicles and solid mechatronics, nanomaterial engineering etc. The proceedings present a selection of 19 papers submitted to the conference from universities, research institutes and industries. All of the papers were subjected to peer-review by conference committee members and international reviewers. Papers included in the proceedings have been selected according to quality and relevance to the conference themes. The proceedings aim to present to the readers recent advances in the field of mechatronics and control engineering and in various related areas. We would like to express our genuine gratitude to everyone who has contributed to ICMCE 2019. The opening remark including a warm welcome to all participants has been given by Prof. Jan Awrejcewicz, The Lodz University of Technology, Poland. Our especially thanks to the conference chair, Prof. Giuseppe Conte, Università Politecnica delle Marche, Italy, the program chairs, Prof. Olivier Sename, Grenoble Institute of Technology, France, Assoc. Prof. Ke-Lin Du, Concordia University, Canada and Prof. Changguo Wang, Harbin Institute of Technology, China, all the technical committee members. Let us wish ICMCE the same success for next year. Conference Chairs Prof. Jan Awrejcewicz, The Lodz University of Technology, Poland Prof. Giuseppe Conte, Università Politecnica delle Marche, Italy
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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 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.477 | 0.345 |
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".