Bibliographic record
Abstract
The Waterloo Workshop on Computer Algebra (WWCA 2006) http://www.cargo.wlu.ca/wwca/ was held April 10 - 12, 2006 at Wilfrid Laurier University in Waterloo, Canada. The workshop was intended to provide a forum for researchers and practitioners in the area of computer algebra. The workshop was devoted to the 60th birthday of Sergei Abramov (Dorodnicyn Computing Centre, Russian Academy of Sciences, Moscow, Russia) whose influential contributions to symbolic methods are highly acknowledged and adopted by many Computer Algebra systems. The invited speakers were•Moulay Barkatou, University of Limoges, France•Jacques Carette, McMaster University, Canada•Jürgen Gerhard, Maplesoft, Canada•Gaston Gonnet, ETH Zurich, Switzerland•Kevin Hare, University of Waterloo, Canada•Claude-Pierre Jeannerod, INRIA Rhône-Alpes, France•Askold Khovanskii, University of Toronto, Canada•George Labahn, University of Waterloo, Canada•Ziming Li, Academy of Mathematics and System Sciences, China•Luc Rebillard, France•Bruno Salvy, INRIA Rocquencourt, France•Arne Storjohann, University of Waterloo, Canada•Serguei Tsarev, Krasnoyarsk State Pedagogical University, Russia•Mark van Hoeij, Florida State University, USA•Stephen Watt, University of Western Ontario, Canada•Thomas Wolf, Brock University, Canada•Doron Zeilberger, Rutgers University, USA
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.463 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".