“Frozen-Ground Cartoons”: Permafrost comics as an innovative tool for polar outreach, education, and engagement
Bibliographic record
Abstract
Abstract Permafrost occupies 20 million square kilometres of Earth’s high-latitude and high-altitude landscapes. These regions are sensitive to climate change and human activities; hence, permafrost research is of considerable scientific and societal importance. However, the results of this research are generally not known by the general public. Communicating scientific concepts is an increasingly important task in the research world. Different ways to engage learners and incorporate narratives in teaching materials exist, yet they are generally underused. Here we report on an international scientific outreach project called “Frozen-Ground Cartoons”, which aims at making permafrost science accessible and fun for students, teachers, and parents through the creation of comic strips. We present the context in which the project was initiated, as well as recent education and outreach activities. The future phases of the project primarily involve a series of augmented reality materials, such as maps, photos, videos, and 3D drawings. With this project we aim to foster understanding of permafrost research among broader audiences, inspire future permafrost researchers, and raise public and science community awareness of polar science, education, outreach, and engagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".