Bibliographic record
Abstract
As one of the most severe and high-impact weather phenomena, tornadoes and their long-term frequency trends have received lots of attention from both the scientific community and the public. Here, we show that over the last six decades (1954–2018), U.S. (E)F1 tornadoes have a statistically significant upward trend but (E)F2–E)F4 tornadoes have statistically significant downward trends based on both solid trend analyses of three independent methods and robust verifications of reported tornado data using a recently developed approach called sample generation by replacement (SGR). Furthermore, we develop a statistical framework to quantitatively explain two-way interconnections between long-term climate trends and internal variabilities. With the support of quantile–quantile plots, we find that a large positive trend of U.S. (E)F1 tornadoes over the last six decades statistically results from a small internal variability of (E)F1 tornado activities. The long-term trends of U.S. (E)F2–(E)F4 tornadoes are also inversely proportional to their internal variabilities, as anticipated by the two-way interconnection theory developed in this study.
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 | 0.001 | 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 | 0.000 | 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".