Science AMA Series: I’m Shiz Aoki, a Science Illustrator with National Geographic Magazine, Hopkins Medicine grad, and founder of Anatomize Studios Inc. AMA!
Bibliographic record
Abstract
Hi reddit! Creating effective visuals to explain your research can be intimidating but also critical to communicating your ideas and findings. I’m passionate about science communication and I’m here today to share a few trade secrets on how to create better journal figures, science illustrations, presentation slides, graphical abstracts and more! All it takes is a few tips and tricks, some help from available tools (or experts!), and a little bit of patience. AMA! Brief bio: Shiz Aoki graduated from the Johns Hopkins University School of Medicine through the Art as Applied to Medicine program after obtaining a B.Sc. in pre-medical sciences, and a Bachelor of Fine Arts and Illustration from Queen’s University in Kingston, Ontario. In 2010, she was hired straight out of school as a science illustrator for National Geographic Magazine at their HQ in Washington, DC. Having grown up in Toronto, she eventually moved back to the city where she continues to actively work for the magazine while operating her own biomedical communications company, Anatomize Studios. She has serviced other renowned clients including Scientific American, HHMI, NIH, McGraw Hill, Stanford University, and many others. Aoki hopes to democratize the process of visual science communication to scientists at all stages of their careers. Her team is currently creating new tools and resources for scientists to create science visuals (such as graphical abstracts, journal figures, presentation slides). Please email shiz@biorender.io if you’re interested in participating or learning more about this new initiative! Follow her on Twitter: @ShizAoki Learn more at www.biorender.io EDIT: Thanks everyone for all the great questions! This was a lot of fun. I’ll glance back in a few days but if you want to chat, please feel free to email me!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.432 | 0.252 |
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".