Visualizing the impacts of Climate Change using AI
Bibliographic record
Abstract
We study the effectiveness of deep learning to help citizens visualize the impacts of climate change where they live, as well as the impact of exposure to AI-generated imagery on subsequent levels of climate change concern, engagement, and policy support. Using a deep learning model based on Generative Adversarial Networks, we are able to generate realistic depictions of climatic events (e.g. extreme flooding) at the local level (e.g. someone's home). We expect that exposure to such highly personalized visual imagery increases concern and can make the problem of climate change seem less distant and more personally relevant for individuals, leading to increased engagement with the issue. Specifically, we examine whether or not, and the extent to which, highly personalized, AI-generated visual depictions of extreme flooding produce greater climate change concern, issue engagement, and support for adaptation and mitigation policies. To examine these questions, we designed a population-based survey experiment that will be administered in Canada (n=2000) and in the United States (n=3000).
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 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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.796 | 0.748 |
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; both teacher heads agree on what is shown here.
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".