Bibliographic record
Abstract
Interventional Neuroradiology 19 (Suppl. 1): 3, 2013 www.centauro.it Past WFITN Meetings 1991 Zurich - Anton Valavanis 1993 Vancouver - Luc Picard 1995 Kyoto - Waro Taki 1997 New York - Alejandro Berenstein 1999 Algarve - Jorge Campos 2001 Seoul - In Sup Choi 2003 Recife - Ronie Leo Piske 2005 Venice - Marco Leonardi 2007 Beijing - Ling Feng 2009 Montreal - Jean Raymond, Daniel Roy, François Guilbert and Alain Weill 2011 Capetown - Allan Taylor and David Lefeuvre 2013 Buenos Aires - Pedro Lylyk and Luis Lemme Plaghos Unfortunately, the name of the president of the second congress WFITN - 1993 was incorrectly listed as Luc Picard instead of Karel ter Brugge in the original publication of this paper. Past WFITN Meetings 1991 Zurich - Anton Valavanis 1993 Vancouver - Karel ter Brugge 1995 Kyoto - Waro Taki 1997 New York - Alejandro Berenstein 1999 Algarve - Jorge Campos 2001 Seoul - In Sup Choi 2003 Recife - Ronie Leo Piske 2005 Venice - Marco Leonardi 2007 Beijing - Ling Feng 2009 Montreal - Jean Raymond, Daniel Roy, François Guilbert and Alain Weill 2011 Capetown - Allan Taylor and David Lefeuvre 2013 Buenos Aires - Pedro Lylyk and Luis Lemme Plaghos Published on line: 30 October 2013 © Centauro s.r.l. 2013 on site: www.interventionalneuroradioloy.it Printed on: 30 September 2013 © Centauro s.r.l. 2013
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.336 | 0.245 |
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".