Bibliographic record
Abstract
It is with great pleasure that we welcome you to the 2022 edition of the International Conference on Graphics and Interaction, held on November 3-4, 2022, at the University of Aveiro, Portugal, as a joint organization with the Eurographics Portuguese Chapter-GPCG.ICGI' 2022 aims to bring together researchers, teachers, and professionals in the areas of Computer Graphics, Image Processing, Computer Vision and Human-Computer Interaction, allowing the dissemination of concluded or ongoing work, as well as the exchange of experiences between the academic, industrial, and end-user communities.Similar to last year, this event includes a Computers & Graphics journal special section on Recent Advances in Graphics and Interaction.Prom the 9 submissions to this special section, 3 were accepted to the Computers &; Graphics journal.The ICGI' 2022 had 31 conference papers (28 long, 3 short) and 3 poster submissions.From the conference papers, 12 were accepted as long papers, 9 as short papers, and 6 invited as posters, following a double-blind review process.These 21 contributions (12 long, 9 short) will be presented at the conference, as well as the 3 accepted posters.Similar to previous years, long papers will be indexed and available at the IEEE Xplore Digital Library.It is also with great pleasure that we thank the presence of the invited keynote speakers, Sergi Bermudez I Badia, from the University of Madeira, Portugal, and Abel Gomes, from the University of Beira Interior, Portugal.Our most sincere thanks for accepting our invitation and enriching this event.We would also like to thank all those who contributed to this event, including the authors, scientific committee members, student volunteers, institutional organizations, and sponsors.We wish you a very productive and exciting event!
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.894 | 0.902 |
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".