Solar Geoengineering, Learning, and Experimentation
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
Solar geoengineering (SGE) can combat climate change by directly reducing temperatures. Both SGE and the climate itself are surrounded by great uncertainties. Implementing SGE affects learning about these uncertainties. We model endogenous learning over two uncertainties: the sensitivity of temperatures to carbon concentrations (the climate sensitivity), and the effectiveness of SGE in lowering temperatures. We present both theoretical and simulation results from an integrated assessment model, focusing on the informational value of SGE experimentation. Surprisingly, under current calibrated conditions, SGE deployment slows learning, causing a less informed decision. For any reasonably sized experimental SGE deployment, the temperature change becomes closer to zero, and thus more obscured by noisy weather shocks. Still, some SGE use is optimal despite, not because of, its informational value. The optimal amount of SGE is very sensitive to beliefs about both uncertainties.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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