Understanding human influence on climate change in China
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
China's climate has been warming since the 1950s, with surface air temperature increasing at a rate higher than the global average. Changes in climate have exerted substantial impacts on water resources, agriculture, ecosystems and human health. Attributing past changes to causes provides a scientific foundation for national and international climate policies. Here, we review recent progress in attributing the observed climate changes over past decades in China. Anthropogenic forcings, dominated by greenhouse gas emissions, are the main drivers for observed increases in mean and extreme temperatures. Evidence of the effect of anthropogenic forcings on precipitation is emerging. Human influence has increased the probability of extreme heat events, and has likely changed the occurrence probabilities for some heavy precipitation events. The way a specific attribution question is posed and the conditions under which the question is addressed present persistent challenges for appropriately communicating attribution results to non-specialists.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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