A cosmic ray-climate link and cloud observations
Bibliographic record
Abstract
Despite over 35 years of constant satellite-based measurements of cloud, reliable evidence of a long-hypothesized link between changes in solar activity and Earth’s cloud cover remains elusive. This work examines evidence of a cosmic ray cloud link from a range of sources, including satellite-based cloud measurements and long-term ground-based climatological measurements. The satellite-based studies can be divided into two categories: (1) monthly to decadal timescale analysis and (2) daily timescale epoch-superpositional (composite) analysis. The latter analyses frequently focus on sudden high-magnitude reductions in the cosmic ray flux known as Forbush decrease events. At present, two long-term independent global satellite cloud datasets are available (ISCCP and MODIS). Although the differences between them are considerable, neither shows evidence of a solar-cloud link at either long or short timescales. Furthermore, reports of observed correlations between solar activity and cloud over the 1983–1995 period are attributed to the chance agreement between solar changes and artificially induced cloud trends. It is possible that the satellite cloud datasets and analysis methods may simply be too insensitive to detect a small solar signal. Evidence from ground-based studies suggests that some weak but statistically significant cosmic ray-cloud relationships may exist at regional scales, involving mechanisms related to the global electric circuit. However, a poor understanding of these mechanisms and their effects on cloud makes the net impacts of such links uncertain. Regardless of this, it is clear that there is no robust evidence of a widespread link between the cosmic ray flux and clouds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".