Attribution and prediction of extreme events: Editorial on the special issue
Bibliographic record
Abstract
The investigation of extreme events and their relation to climate change and variability is arguably one of the most challenging areas in climate research. Extremes are also of continual concern amongst policy and decision makers, and in the media, because of the devastating impacts that can result from their occurrence. As highlighted in the recent IPCC Special Report on “Managing the risks of extreme events and disasters to advance climate change and adaptation” (SREX), climate extremes are by definition rare, often difficult to define, not well observed, and pose substantial challenges in their characterization (IPCC, 2012, Seneviratne et al., 2012). The scientific challenges are multidisciplinary, and point to the need for improved statistical analysis tools, deeper process understanding, and more extensive assessments of the societal and ecosystems impacts of extreme events. Despite, or maybe because of these issues, research on extremes is vibrant and is advancing rapidly (Seneviratne et al., 2012, Zwiers et al., 2013, Herring et al., 2014). Nevertheless, the magnitude and importance of research on extremes is so large that even greater levels of effort and amounts of expertise are urgently required across a broad range of disciplines.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".