Bibliographic record
Abstract
The purpose of the three-day 2023 2nd International Conference on Acoustics, Fluid Mechanics and Engineering (AFME 2023), held during November 17th to 19th, 2023 in Nanjing, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on acoustics, fluid mechanics and engineering. Highly eventful program of the Conference, the list of well-established organizations-participants and devoted engagement of many colleagues throughout all the stages of the Conference preparation instill confidence in practical importance of mutual initiative. As usual, the program of the meeting mainly consisted of plenary speeches, invited speeches, oral presentations and poster presentations, discussing significant problems facing technologies related to areas of acoustics, fluid mechanics and engineering, proposing innovative ideas and approaches to solution of the problems, and considering new possibilities of application and development of cutting-edge technology. In the framework of experimental and theoretical approaches, the Conference gathered 150 delegates from all over the world and addressed a number of highly relevant aspects of acoustics, fluid mechanics and engineering. Covering topics on Acoustic Materials, Hydrodynamic Acoustics, Ultrasonics, Physical Chemical Hydrodynamics, Hydromechanical Hydrodynamics, Engineering Fluid Mechanics, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of acoustics, fluid mechanics and engineering should be considered. We received various manuscripts of invited papers and other contributions presented at the Conference. All of these papers have gone through a rigorous peer review process and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by relevant communities. We would like to express our thanks to all the authors for their time and genuine contributions, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Guangdong University of Technology, Dalian Maritime University, School of Marine Science and Technology, Northwestern Polytechnical University, and University of Toronto. List of Committee Member is 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 | 0.012 |
| 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) | 0.516 | 0.368 |
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".