Bibliographic record
Abstract
The International Symposium on Structural Dynamics of Aerospace (ISSDA) was held on September 9-10 2023, hosted by Northwestern Polytechnical University. The conference sponsored by Chinese Society of Space Research, Harbin Institute of Technology, Tongji University, Xi’an Jiaotong University, Xi’an Jiaotong University etc. The ISSDA 2023 brings together worldwide scientists and engineers for sharing new research results and cutting-edge technologies, accelerating future development and applications relevant to aerospace structural dynamics, control, and related areas. The technical program of ISSDA 2023 will include Keynote Speeches, Oral Presentations, and Poster Presentations. The keynote speaker include Prof Bangchun Wen, Academician of Chinese Academy of Sciences, China. Prof Zuo Mingjian, Fellow of the Canadian Academy of Engineering. Prof Asoke Nandi, Fellow of the Royal Academy of Engineering. Prof. Ruqiang Yan, Xi’an Jiaotong University, China (IEEE Fellow, ASME Fellow). Prof. Vincent Laude, Univ. Bourgogne Franche-Comté and CNRS, France. More than 340 participants were able to exchange knowledge and discuss the latest developments at the conference. The book contains 106 peer-reviewed papers, selected from more than 380 submissions and ranging from the theoretical and conceptual to strongly pragmatic and addressing industrial best practice. List of Committee 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.508 | 0.379 |
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".