Computing the real solutions of polynomial systems with the RegularChains library in Maple
Bibliographic record
Abstract
demonstration Share on Computing the real solutions of polynomial systems with the RegularChains library in Maple Authors: Changbo Chen University of Western Ontario, Canada University of Western Ontario, CanadaView Profile , James H. Davenport University of Bath, UK University of Bath, UKView Profile , François Lemaire Université de Lille 1, France Université de Lille 1, FranceView Profile , Marc Moreno Maza University of Western Ontario, Canada University of Western Ontario, CanadaView Profile , Bican Xia Peking University, China Peking University, ChinaView Profile , Rong Xiao University of Western Ontario, Canada University of Western Ontario, CanadaView Profile , Yuzhen Xie University of Western Ontario, Canada University of Western Ontario, CanadaView Profile Authors Info & Claims ACM Communications in Computer AlgebraVolume 45Issue 3/4September/December 2011 pp 166–168https://doi.org/10.1145/2110170.2110174Online:23 January 2012Publication History 5citation95DownloadsMetricsTotal Citations5Total Downloads95Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".