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Preface

2024· article· en· W4396771193 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

The purpose of the three-day 2023 2 nd International Conference on Acoustics, Fluid Mechanics and Engineering (AFME 2023), held during November 17 th to 19 th , 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.256
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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