SAMPTA'09, International Conference on SAMPling Theory and Applications
Bibliographic record
Abstract
ORGANIZING COMMITTEE: General chairs: • Laurent Fesquet (TIMA, Grenoble Institute of Technology) • Bruno Torrésani (LATP, Université de Provence, Marseille) Local committee • Sandrine Anthoine (I3S, CNRS, SophiaAntipolis) • Karim Kellay (LATP, Université de Provence, Marseille) • Matthieu Kowalski (LATP, Université de Provence, Marseille) • Clothilde Melot (LATP, Université de Provence, Marseille) • El Hassan Youssfi (LATP, Université de Provence, Marseille PROGRAM COMMITTEE: Program chairs • Yonina Eldar (Electrical Engineering, Technion, Israel Institute of Technology) • Karlheinz Gröchenig (Fakultät für Mathematik, University of Vienna) • Sinan Gunturk (Mathematics Department, Courant Institute, New York) • Michael Unser (Biomedical Imaging Group, EPFL Lausanne) Special sessions organizers • Bernhard Bodmann (Dept of Mathematics, University of Houston, USA) • Pierluigi Dragotti (Dept of Electronic and Alectrical Engineering, Imperial College London, UK) • Yonina Eldar (Electrical Engineering, Technion, Israel Institute of Technology, Israel) • Laurent Fesquet (TIMA, INP Grenoble, France) • Massimo Fornasier (RICAM, Linz University, Austria) • Hakan Johansson (Dept of Electrical Engineering, Linkoping University, Sweden) • Gitta Kutyniok (Universität Osnabrück, Germany) • Pina Marziliano (Nanyang Technological University, Singapore) • Götz Pfander (International University Bremen, Germany) • Hölger Rauhut (Hausdorff Center for Mathematics, University of Bonn, Germany) • Jared Tanner (School of Mathematics, University of Edimburgh, Scotland) • Christian Vogel (ISI, ETH Zurich, Switzerland) • Ozgur Yilmaz (Dept of Mathematics, University of British Columbia, Vancouver, Canada)
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".