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
If our planet is going to survive the climate crisis, we need to act rapidly. Taking cues from progressive cities around the world, including Los Angeles, New York, Toronto, Oslo, Shenzhen, and Sydney, this book is a summons to every city to make small but significant changes that can drastically reduce our carbon footprint. We cannot wait for national governments to agree on how to reduce greenhouse gas emissions and manage the average temperature rise to within 1.5 degrees. In Solved, David Miller argues that cities are taking action on climate change because they can - and because they must. The updated paperback edition of Solved: How the World's Great Cities Are Fixing the Climate Crisis demonstrates that the initiatives cities have taken to control the climate crisis can make a real difference in reducing global emissions if implemented worldwide. By chronicling the stories of how cities have taken action to meet and exceed emissions targets laid out in the Paris Agreement, Miller empowers readers to fix the climate crisis. As much a "how to" guide for policymakers as a work for concerned citizens, Solved aims to inspire hope through its clear and factual analysis of what can be done - now, today - to mitigate our harmful emissions and pave the way to a 1.5-degree world
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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