Bibliographic record
Abstract
The 20th International Conference on Aerospace Sciences & Aviation Technology ASAT-20 MAY 9 - 11, 2023, Cairo, Egypt The Military Technical College, Cairo, Egypt, has the pleasure to organize the 20thInternational Conference on Aerospace Sciences & Aviation Technology (ASAT-20), MAY 9 - 11, 2023. The Organizing Committee welcome all the Aerospace Sciences and Aviation Technology professionals to this signature event in Military Technical College, looking forward to outstanding meetings with top industrial, academic scientists, and researchers; from the different countries around the world; to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Aerospace Sciences and Aviation Technology. The conference has been received 160 scientific papers., The reviewe process starts with plagiarism test and reviewed by 120 specialized professors of different conference fields. A 94 scientific papers are approved, and they will be discussed in 25 scientific sessions. As part of the conference activities, 12 scientific lectures will be held on the latest findings of scientists in the field of aerospace engineering and their applications. The researchers and professors represent many research and academic centers and industrial entities from the Arab Republic of Egypt, the United States of America, China, Canada, the United Kingdom, Germany, India, Pakistan and the Czech Republic. List of Conference Topics, Conference Committee, Contributors, Conference Schedule, Scientific Sessions, Workshops and Keynotes Speakers, List of Reviewers, Authors Index 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.496 | 0.347 |
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".