California Getting Wetter to the North, Drier to the South: Natural Variability or Climate Change?
Bibliographic record
Abstract
Current climate change projections anticipate that global warming trends will lead to changes in the distribution and intensity of precipitation at a global level. However, few studies have corroborated these model-based results using historical precipitation records at a regional level, especially in our study region, California. In our analyses of 14 long-term precipitation records representing multiple climates throughout the state, we find northern and central regions increasing in precipitation while southern regions are drying. Winter precipitation is increasing in all regions, while other seasons show mixed results. Rain intensity has not changed since the 1920s. While Sacramento shows over 3 more days of rain per year, Los Angeles has almost 4 less days per year in the last century. Both the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) greatly influence the California precipitation record. The climate change signal in the precipitation records remains unclear as annual variability overwhelms the precipitation trends.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".