The Detection and Attribution of Human Influence on Climate
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
This article describes the field of the detection and attribution of climate change and highlights recent progress, major issues, and future directions. The attribution of global temperature variations over the past century to a combination of anthropogenic and natural influences is now well established, with the anthropogenic factors dominating. Other aspects of the climate system, including regional quantities, are increasingly being found to also show a detectable signal of human influence. Of particular interest, though, is the attribution of changes in nonmeteorological quantities, such as hydrological and ecological measures, and of changes in the risk of extreme weather events to anthropogenic emissions. Methods are being developed for tackling these two problems but are still in the early stages. As the field gradually includes a service focus, the biggest challenges will become the integration of various approaches into an overall framework and the communication of the capabilities and limitations of that framework to the outside community.
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 | 0.001 | 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) | 0.000 | 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