Bibliographic record
Abstract
This chapter begins with a grim but tangible example of the effect of unprecedented weather extremes: the Australian heatwave and wildfires of 2009, which claimed more than 600 lives. As these unfortunate disruptions to weather increase (one of the many upshots of global warming), scientists and governmental bodies must improve their ability to predict these events, in the absence of real political change. The stakes could not be higher; the European heatwave of 2003, for example, resulted in up to 70,000 deaths. The chapter highlights improvements in weather prediction, with most national services able to predict a week in advance, more accurately than forecasters in the 1970s could predict a day. This allows for greater multi-agency responses to weather emergencies and better modeling of the effects of global warming on weather trajectories. While the changing frequency of temperature extremes over the past fifty years is palpable, patterns in other extreme weather events are more difficult to identify, and this chapter unpacks various reasons why this is the case. In turn, improvements in seasonal climate forecasting (e.g., forecasting El Niño) will enable us to respond more effectively to the consequences of drought and other inter-annual weather variability, through crop management and timelier deployment of food relief.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.393 | 0.237 |
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".