Cascading impacts of extreme events across an interconnected and warming world
Bibliographic record
Abstract
Extreme events (e.g., heatwaves, wildfires, droughts, floods, etc.) are anticipated to become more severe, persistent, and frequent throughout many parts of the world due to warming. Such extreme events occur across a diverse set of ecosystems and climatic regions and their multifaceted impacts cascade in space, time, and across sectors (e.g., water, energy, agriculture, economic, human health). To better understand the cascading impacts of extreme events and their feedbacks, we draw on recent examples such as the 2023 heatwaves and wildfires in Canada. In addition, we also examine other extreme events (e.g., droughts, floods) around the world and their feedbacks and interactions that pose challenges for modeling, monitoring, and managing associated risks. For example, we quantify how snowpack changes and drought across agricultural regions have wide-reaching impacts that affect remote areas. Our study highlights that the impacts of extreme events have important feedbacks that should be considered in resource and risk models and management as well as remote impacts that are not yet fully understood or well-tracked. Furthermore, we identify other challenges, existing knowledge gaps, and future directions to guide global monitoring and modeling of impact cascades for improved mitigation, adaptation, and climate change resilient policy advancements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".