Bibliographic record
Abstract
These proceedings contain the scholarly papers presented in two reputable joint conferences, the 5th International Conference on Aeronautical, Aerospace and Mechanical Engineering (AAME 2022). AAME have been held in different parts of the world for some years, as indicated by the number sequence. This year (2022), the conference was scheduled to be held in Haikuo, China. However, due to unexpected surge globally in COVID-19 variant in the last three months, for safety and also travel restriction reasons, it is held virtually and all participants can attend AAME conference via “Zoom”. This conference has invited keynote speakers who are professors from renowned universities in China, Canada and Russia. Delegates from around the world including China, Bulgaria, South Africa, Canada, Russia and Australia took the opportunity to share their research results and discuss potential scientific and engineering development from their work. Facilitation of the International Technical Committee which consist of members from universities and research organisations around the world is vital to the success of these conferences. All papers in these proceedings have passed the vigorous review process involving reviewers of the International Technical Committee. Authors benefited from valuable comments and improved their submissions to the satisfaction of reviewers. The virtual presentation serves as another opportunity for the conference delegates to address critiques in the real time online face-to-face meetings with the expert audience. List of Conference Committees 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 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.468 | 0.373 |
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".