Walking in the Cold: AI-Generated depictions of warming permafrost
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d8935778e66">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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it