Supporting regional and international cooperation in research on extremes in climate prediction and projection ensembles: Workshop summary
Bibliographic record
Abstract
Weather and climate extremes have enormous impacts on society, and are becoming more severe and frequent as the world warms. Most developing countries in the Asia-Pacific region are highly vulnerable to risks associated with heatwaves and cold spells, droughts and floods, tropical cyclones, wildfires, and other extremes. To support regional and international cooperation for research on weather and climate extremes in the Asia-Pacific region, the World Climate Research Programme (WCRP) hosted an online workshop on Extremes in Climate Prediction Ensembles (ExCPEns) from 25 to 28 October 2021 with the support of Asia-Pacific Network for Global Change Research (APN). The workshop aimed to advance the rapidly emerging science of exploiting subseasonal, seasonal, annual to decadal and long-term prediction ensembles to improve the prediction and understanding of weather and climate extreme events. An Early Career Scientist (ECS) event followed the ExCPEns workshop and consisted of a discussion and networking forum for ECS from APN member developing countries, along with a series of ECS training lectures and discussion sessions. Through the workshop and discussions among stakeholders, important scientific results on prediction and future changes in weather and climate extremes were communicated. Moreover, new research topics spanning these different time scales were identified and prioritized.
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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 | 0.026 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".