Defining Extreme Events with Practicable Monitoring Thresholds
Bibliographic record
Abstract
The term “extreme event” is frequently applied in policy, media, and colloquial settings. The Pipeline and Hazardous Materials Safety Administration (PHMSA) Mega Rule presents requirements for natural gas transmission pipeline operators to inspect their pipelines following an “extreme weather event” or “natural disaster.” However, these terms are poorly defined, and stakeholders have expressed concern that identifying the onset/cessation of an extreme event is unclear, muddying compliance. Here, we present a framework and thresholds for defining an “extreme event” as major streamflow, rainfall, or an earthquake that produces large-scale geomorphic change, rather than “associated hazards” like landslides and channel scour that directly produce ground deformation. Thresholds for streamflow and rainfall are defined in terms of frequency, where the 1% exceedance storm or flood (i.e., the 100-year event) is considered “extreme.” Earthquakes are classified as “extreme” with absolute ground shaking thresholds over which the probability of pipeline damage is considered non-negligible. The definitions of “extreme event” outlined in this paper were formulated within the context of pipeline integrity management but are potentially applicable to other buried or aboveground infrastructure.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".