Bibliographic record
Abstract
After this long pandemic, we are thrilled to see the return of in-person participation to the international NEWCAS conference, with most attendees expected on site in Quebec City.This year marks the 20th edition, after the Quebec Strategic Alliance for Microsystems Research Center (ReSMIQ) launched the first one in 2003 in Montreal and placed it under the co-sponsorship of the IEEE Circuits and Systems Society (CASS) in 2004.Since its auspicious beginning, it has progressively grown to become a major scientific and technical event and is now a full-fledge IEEE CASS conference, with ReSMiQ still playing a key role in its organization.This world-class forum of exchange is back in the Province of Quebec this year, after taking place virtually in Toulon (France) in 2021.It has followed this alternating pattern between France and Quebec since 2009, apart from the 2016 edition in Vancouver (Canada), and Munich (Germany) for the 2019 edition, after Toulouse in 2009, Montreal in 2010, Bordeaux in 2011, Montreal in 2012, Paris in 2013, Trois-Rivières in 2014, Grenoble in 2015, Strasbourg in 2017 and Montreal in 2018 and 2020.We are also proud that, since the 2020 edition, NEWCAS is now an IEEE CASS interregional flagship conference, and we thank all the persons who made this a reality.This 20 th edition offers the opportunity to reflect on two decades of scientific innovation and strengthen the relevance of the NEWCAS conference, while also looking ahead.The large spectrum of research areas covered this year attracted two hundred and nine submissions from twenty-nine countries, notwithstanding the papers in five special sessions and one mini-symposium.They were sorted in seventeen tracks and sent to many Technical Committee members and external reviewers who conducted a thorough evaluation of each paper.We are most grateful for their invaluable help, which allowed us to raise the quality of NEWCAS to the high level expected by our community.There were 3.4 evaluations per paper
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.766 | 0.659 |
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".