Advanced Imaging and Microscopy in Life and Natural Sciences as a Research Tool for Illustration [Presentation for 15th International Illustration Research Symposium: The role of technology in illustration, 21-22 November 2025. Koç University, Istanbul]
Bibliographic record
Abstract
2025 at Koç University in Istanbul. This year’s theme, The Apparatus: The Role of Technology in Illustration, invites us to explore how technological developments shape, challenge, and expand the field of illustration. Our invited speakers will engage with the symposium theme from a wide range of perspectives, illuminating both historical and contemporary ideas. They will also explore the dynamic interplay between illustration, art, design and technology, including the examination of the growing influence of artificial intelligence within the field of illustration.Further, the symposium will also present across-disciplinary workshop led by chemists, inviting participants to reimagine scientific illustration software through an artistic lens. This collaborative session will demonstrate how scientific and artistic practices can inform, enrich, and inspire one another, highlighting the creative potential that emerges when disciplines intersect.In addition, sixty poster presentations by emerging, mid-career, and established practitioners and scholars will offer further visual and textual explorations of the symposium’s central theme. This symposium brings together a vibrant community of illustrators, researchers, and scholars from Belgium, Bulgaria, Canada, China, France, Georgia, Germany, Greece, Hong Kong, Hungary, India, Ireland, New Zealand, Pakistan, Poland, South Africa, Switzerland, the United Kingdom, the United States, and Türkiye. Together, these contributions from different parts of the world aim to open new pathways for cross-cultural dialogue, foster cross-disciplinary collaboration, and deepen our collective understanding of the evolving role of technology in illustration Presentation panel: Theme 1 - Approaches in Imaging and Visualization < Provocation: If science and art now see through the same mechanical eyes, what kind of truths and fictions emerge from this hybrid vision where human imagination and machine intelligence co-draw reality?Chair: Nanette Hoogslag10:50 – 11:15Speaker 1 - Joanne Berry-FrithAdvanced Imaging and Microscopy in Life andNatural Sciences as a Research Tool forIllustration11:15 – 11:40Speaker 2 - Nurcan Tunçbağ, Özlem Keskin,Attila GürsoyVisualization of Proteins and TheirInteractions Through Illustration or Animation11:40 – 12:05Speaker 3 - Balca Arda, Ceren İlikan RasimoğluMedical Visual Creative Discourse fromTuberculosis to COVID-19 in Türkiye12:05 – 12:30Speaker 4 - Sheryl N. HamiltonReading Viral Portraits as Disease Media.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".