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Preface

2023· article· en· W4386865364 on OpenAlexaboutno aff
Nader Asnafi

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGratitudeChinaLibrary scienceEvent (particle physics)ConstructivePolitical scienceEngineeringEngineering ethicsMedical educationPublic relationsPsychologyMedicineLawComputer science

Abstract

fetched live from OpenAlex

Over the last 5 years, MEIE has been held in various locations, including Hangzhou (2018 & 2019), online conferences (2020 & 2022), and hybrid event in Kunming (2021), attracted participants from 14 countries and regions including Sweden, Canada, the United States, Australia, China, Malaysia, Singapore, South Korea… The Sixth International Conference on Mechanical, Electric and Industrial Engineering (MEIE 2023) was co-organized by China Institute for Quality Research (Shanghai Jiao Tong University) and Faculty of Mechanical Engineering & Mechanics, Ningbo University, which was held successfully during May 23-25, 2023 in Sanya, China. This event attracted about 70 participants from all over the world, and addressed both basic research and the societal/industrial-technological needs within mechanical, electric and industrial engineering. Conference program was divided into 3 sessions: keynote speeches, oral presentations and poster presentations. We are honored to invite 3 experts to give the impressive lectures. Following the keynote speakers, there were 20 oral presenters and 13 poster presenters. During these sessions, presenters shared their latest achievements. Audiences were actively participated as well. The accepted papers have been through rigorous peer review to meet the requirements of international publication standards. Many thanks to the authors for their valuable contributions and to the attendees for their active participation. We would like to express our gratitude to the reviewers, who provided constructive criticism and stimulating comments and suggestions to the authors. We are grateful to the organizers, technical program committee for their precious time and advice, as well as internationally renowned scientists who acted as keynote speakers at the conference. Finally, our sincere gratitude goes also to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the conference proceedings. On behalf of the Organizing Committee of MEIE 2023. List of Technical Program Committee are 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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.549
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.4510.318

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 head0.021
GPT teacher head0.233
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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