Statistical evidence about human influence on the climate system
Bibliographic record
Abstract
We use recent methods for the analysis of time series data, in particular related to breaks in trends, to establish that human factors are the main contributors to the secular movements in observed global and hemispheric temperatures series. The most important feature documented is a marked increase in the growth rates of temperatures (purged from the Atlantic Multidecadal Oscillation) and anthropogenic greenhouse gases occurring for all series around 1955, which marks the start of sustained global warming. Also evidence shows that human interventions effectively slowed global warming in two occasions. The Montreal Protocol and the technological change in agricultural production in Asia are major drivers behind the slowdown of the warming since 1994, providing evidence about the effectiveness of reducing emissions of greenhouse gases other than CO2 for mitigating climate change in the shorter term. The largest socioeconomic disruptions, the two World Wars and the Great Crash, are shown to have contributed to the cooling in the mid 20th century. While other radiative factors have modulated their effect, the greenhouse gases defined the secular movement in both the total radiative forcing and the global and hemispheric temperature series. Deviations from this anthropogenic trend are shown to have transitory effects.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".