Three Ways Forward to Improve Regional Information for Extreme Events: An Early Career Perspective
Bibliographic record
Abstract
This paper provides an early career researchers (ECRs) perspective on major challenges and opportunities that arise in the study and understanding of, and the provision of regional information for Climate, Weather and Hydrological (CWH) extreme events. This perspective emerged from the discussions of the early career three-day Young Earth System Scientists - Young Hydrologic Society (YESS-YHS) workshop, which was conjointly held with the Global Energy and Water Exchanges (GEWEX) Open Science Conference. In this paper we discuss three possible ways forward in the field: a stronger interaction between Earth system scientists and users, a collaborative modelling approach between the different modelling communities, and an increased use of unconventional data sources in scientific studies. This paper also demonstrates the important role of ECRs in embracing the above outlined pathways and addressing the long-standing challenges in the field. YESS and YHS networks encourage the global community to support and strengthen their involvement with ECR communities to advance the field of interdisciplinary Earth system science in the upcoming years to decades.
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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 | 0.085 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".