Poisson Geometry and Applications
Bibliographic record
Abstract
The half-size workshop was organized by Anton Alekseev (Geneve), Rui Loja Fernandes (Lisboa), Eckhard Meinrenken (Toronto) and Markus Pflaum (Frankfurt). Marius Crainic (Utrecht) also acted as an unofficial organizer. The programme consisted of 17 lectures and covered a range of areas in Poisson Geometry and its applications where significant progress has been achieved recently. The aim of the workshop was to emphasize the main themes in Poisson Geometry that play the role of driving forces and organizing principles of the field. A significant number of young researchers, who have made already important contributions to the field, participated in this meeting. During the workshop all participants were involved in a great number of informal discussions, some of which gave rise to new collaborations. In total, 27 researchers have participated in this meeting from institutions in 9 different countries in Europe, USA and Canada, including 4 researchers from German institutions. The organizers and participants thank the Mathematisches Forschungsinstitut Oberwolfach for providing a truly inspiring atmosphere for this conference. In the following we include the abstracts in alphabetical order.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".