How summer 2021 has changed our understanding of extreme weather
Bibliographic record
Abstract
A succession of record-breaking natural disasters have swept the globe in recent weeks. There have been serious floods in China and western Europe, heatwaves and drought in North America and wildfires in the sub-Arctic. An annual report on the UK’s weather indicates extreme events are becoming commonplace in the country’s once mild climate. August 2020 saw temperatures hit 34°C on six consecutive days across southern England, including five sticky nights where the mercury stayed above 20°C. In the future, British summers are likely to see temperatures greater than 40°C regularly, even if global warming is limited to 1.5°C. The Canadian national temperature record was shattered in June 2021 meanwhile, with 49.6°C recorded in Lytton, British Columbia – a town that was all but destroyed by wildfires a few days later. Many of these events have shocked climate scientists. The Lytton temperature record, for example, was head-and-shoulders above those set during previous heatwaves in the region. Some scientists are beginning to worry they might have underestimated how quickly the climate will change. Or have we just misunderstood extreme weather events and how our warming climate will influence them?
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".