Water Whiplash in Mediterranean Regions of the World
Bibliographic record
Abstract
The presence of weather and water whiplash in Mediterranean regions of the world is analyzed using historical streamflow records from 1926 to 2023, depending on the region. Streamflow from the United States (California), Italy, Australia, Chile, and South Africa is analyzed using publicly available databases. Water whiplash—or the rapid shift of wet and dry periods—are compared. Wet and dry periods are defined based on annual deviations from the historical record average, and whiplash occurs when there is an abrupt change that overcomes an accommodated deficit or surplus. Of all the stations, there are more dry years (56%) than wet years (44%) in these regions, along with similarities in the variances and shifts in extremes (i.e., whiplash). On average, 35% of the years were defined as water whiplash years in all countries, with the highest levels in the US (California), where 42–53% of the years were whiplash years. The influence of the El Niño–Southern Oscillation (ENSO) influences Chile and South Africa strongest during the first quarter of the year. This study found that smaller extreme wet periods and larger and less extreme dry periods are prevalent in Mediterranean regions. This has implications for water management as adaptation to climate change is considered.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".