Bibliographic record
Abstract
From 18 to 20 October 2004, a conference “ Singularities and Computer Algebra ” was held at the University of Kaiserslautern on the occasion of Gert-Martin Greuel's 60th birthday. It was attended by 70 participants from Europe, Israel, Japan, Canada and the U.S.A. We were particularly happy that Greuel's teacher, Egbert Brieskorn, was among them. Most of the participants have been inuenced by Greuel's work on singularities and their computational aspects over the last 30 years. Among them, one could find colleagues and friends from the early years in Göttingen and Bonn, but also former and present diploma and Ph.D. students of Gert-Martin Greuel at Kaiserslautern. In particular, each of the invited speakers could look retrospectively at cooperating in one way or another with Greuel. The papers of this volume concern ten of the invited lectures, supplemented by four articles which are written by participants of the conference and focus on computational aspects. Most of the contributions are intended to give an overview on a particular aspect of singularities. They describe the development of important areas of singularity theory over the past years and they discuss open questions. In the lead text, we include a list of the invited lectures and a list of the participants as well as a picture of the septic with 99 nodes found by Oliver Labs and Duco van Straten, which has acted as a logo for the conference. Further, we include an article focussing on Aspects of Gert-Martin Greuel's Mathematical Work .
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.501 | 0.311 |
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".