Frozen-Ground Cartoons : An international collaboration between artists and permafrost scientists
Bibliographic record
Abstract
This project started in October 2015 with a crazy idea : prepare and submit a funding application for an international, multidisciplinary and non-traditional scientific outreach project… within the next 48 hours. Well, it worked out. A group of highly motivated young researchers from Canada and Europe united to combine arts and science and produce a series of outreach comic strips about permafrost (frozen ground). The aim of the project is to present and explain scientific research conducted across the circumpolar Arctic, placing emphasis on field work and the rapidly changing northern environment. The target audience is kids, youth, parents and teachers, with the general goal of making permafrost science more fun and accessible to the public. Because guess what : permafrost represents an area of more than twenty million km2 in the Northern Hemisphere, a huge area. As the climate warms, permafrost thaws and becomes unstable for houses, roads and airports. This rapid thawing of previously frozen ground also disrupts plant and animal habitats, impacts water quality and the ecology of lakes, and releases carbon into the atmosphere as greenhouse gases, making climate change even stronger. Hence permafrost and its response to climate change concerns us all. The project received initial support from the International Permafrost Association (IPA) as a targeted ‘Action Group’, and since then several other sponsors have joined the project. Here we are, now, two years after this first idea. What you are about to read is the result of an iterative process of exchanging ideas between artists and scientists. We first made an application call and received 49 applications from artists in 16 countries. Through a formal review process, we then selected two artists to work on this project: Noémie Ross from Canada, and Heta Nääs from Finland. With input from scientists, Noémie and Heta created fantastic cartoons that explain some of the changes happening to the environment in permafrost areas, how they affect people and wildlife, and what scientists are doing to better understand these changes to help people find innovative ways to adapt. We wish everyone plenty of fun reading this booklet and we would like to thank all those who supported this project.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".