Managing extremes in the Anthropocene: a causal, stochastic physics approach
Bibliographic record
Abstract
Anthropogenic activities have led to an alarming rise in frequencies and severities of hydroclimatic extremes, resulting in deaths, adverse health impacts, near-destruction of municipalities, and trillions of US dollars or equivalent in economic impacts worldwide. With trends expected to continue or intensify, there is growing pressure to re-evaluate current adaptation and mitigation strategies. Here, it is demonstrated that while much of risk management, particularly of floods, remains magnitude-centric, a frequency-centric approach is key for reliably predicting risks and designing cost-effective solutions. This approach requires bridging hydroclimatology’s extreme value theory with the modern science of causation through stochastic physics. It anchors physical understanding in probabilistic frameworks, centring environmental and anthropogenic controls when modelling and interpreting frequency distributions. Linking natural controls to a system’s undisturbed state and subsequent frequency-centric quantification of anthropogenic influence highlights large frequency changes far surpassing changes in magnitude, aligning scientific and professional practice with reality. In fact, the dramatic rise in extremes likely arises in part as a manifestation of an undisturbed distribution that is inherently highly sensitive to anthropogenic change, a crucial question for risk management. Only with an accurate portrayal of risk and sensitivity grounded in the deeper meaning of probability can hydroclimatology motivate strong policies and support the design of innovative solutions in the Anthropocene.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".