Technical Committee's member of "The 4th INTERNATIONAL WORKSHOP ON FUNDAMENTAL ISSUES, APPLICATIONS AND FUTURE RESEARCH DIRECTIONS FOR PARALLEL MECHANISMS / MANIPULATORS / MACHINES," 9-11 September 2020, Belfast (UK)
Bibliographic record
Abstract
In 2002, Clément Gosselin and Imme Ebert-Uphoff organized the first conference on parallel mechanisms and manipulators in Québec, Canada as a fruitful and friendly meeting. Six years after, in 2008, François Pierrot, Nicolas Andreff, Olivier Company, Marc Gouttefarde and Sébastien Krut organised the second edition in Montpellier (France). In 2014, Tian Huang and Jian Dai organized the third edition in Tianjin China. Following this success, and another six years on, the fourth edition in 2020 in Belfast, UK is a platform for us to share our interest and discuss the future directions in parallel mechanisms and manipulators. The conference seeks to bring together experts in the area from around the globe in order to disseminate recent research progress and to identify the most promising future research directions for parallel manipulators and parallel kinematic technology. The meeting is a series of focused keynote presentations, round-tables, focused paper sessions and in-depth discussion sessions to converge on key issues related to parallel mechanisms, manipulators and parallel kinematic machines.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.071 |
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".