Walking in the Cold: AI-Generated depictions of warming permafrost
Bibliographic record
Abstract
The "Walking in the Cold" project, developed by the Critical Future Studio/Lab at the University of British Columbia, leverages artificial intelligence (AI) to visualize the consequences of permafrost warming in northern Canada as a means of effectively communicating climate change implications. As climate change threatens to reshape northern Canadian landscapes, psychological distance hinders public engagement—climate change is often perceived as a remote issue in terms of time, space, and relevance. To address this challenge, the team combines syndicated climate data from governmental sources with ChatGPT's AI capabilities to create vivid, data-driven visual narratives. By training ChatGPT with climate data, the project generates tailored prompts for AI text-to-image generators, producing images grounded in verifiable data and reflecting future landscapes under the threat of climate change. The project's methodology involves acquiring, curating, and transforming climate data into compelling visuals. The AI-generated images aim to present objective foresights, fostering a deeper connection with the subject matter and eliciting emotional resonance in viewers. These visuals also demonstrate the intricate role of the permafrost in the ecosystem.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".