Sustainability Nexus AID: storms
Bibliographic record
Abstract
Abstract Storms include a range of weather events resulting in heavy liquid and solid precipitation and high winds. These events critically impact crops and natural resources and, in turn, health, economy, and infrastructure safety. The intensity and frequency of the physical mechanisms triggering storms will most likely increase under global warming due to the changing flows of water and energy in the atmosphere. Addressing storm threats holistically requires a nexus approach that links climate change, infrastructure, and human prosperity and well-being, contributing to achieving the UN’s Sustainable Development Goals. This work introduces the Storms Module of the United Nations University (UNU) Sustainability Nexus Analytics, Informatics, and Data (AID) Programme. The paper aims to emphasize the importance of AID tools in addressing storm impacts through a data-driven nexus approach that recognizes the connections between storm hazards, policy, and society. Today, AID tools are instrumental in understanding storms and making informed decisions to manage them. AID tools contribute to archiving and monitoring storm data, employing predictive models and early warning systems, estimating storm risk, conducting post-storm analysis, and aiding preparedness, response, and recovery efforts. The Storms Module lists freely available AID tools, including large databases, simulation and precipitation tools, and resources for storm preparedness. Over the next years, new Artificial Intelligence (AI) technologies, are expected to revolutionize storm understanding, forecasting, and adaptive planning. However, especially for the operational use of new AI tools, caution is advised due to potential limitations regarding data quality, ethical concerns, cybersecurity risks, and the need for legal frameworks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".