Letter of thanks for <i>IJMSD</i>'s indexing in ESCI
Bibliographic record
Abstract
Dear Authors/Reviewers/Editorial Board Members/Editorial Office Members/Readers, We are delighted to inform you that the International Journal of Mechanical System Dynamics (IJMSD) was officially indexed by Emerging Sources Citation Index (ESCI) on June 27, 2023, after being indexed by Inspec, Scopus, DOAJ, Dimensions, and some other databases. We would like to take this opportunity, on behalf of the IJMSD Editorial Board, to extend our gratitude and sincere appreciation for your significant contributions and support to IJMSD. The mission of IJMSD is to provide cutting-edge scientific and technology research in the area of mechanical system dynamics, with the goal of enhancing modern industrial research and development capabilities to improve the performance of mechanical systems. The research published in the journal highlights the crucial role of mechanical system dynamics throughout the entire lifecycle of modern and complex engineering products. The journal covers a wide range of topics, including theories, modeling, computation, analysis, software, design, control, manufacturing, testing, and evaluation of mechanical system dynamics. Since IJMSD's launch in July 2021, three volumes, eight issues, and 64 papers have been published. All these papers are open-access and available at https://onlinelibrary.wiley.com/loi/27671402. Authors are welcome to submit high-quality papers to IJMSD and are encouraged to read the already published IJMSD papers. With your help and contributions, IJMSD will become the leading scientific journal in the field of mechanical system dynamics. Your continuous support and contributions to the journal are highly appreciated.
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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 | 0.010 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.132 | 0.166 |
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".