Reducing the Extreme in Extreme Events by Knowing What Is Possible
Bibliographic record
Abstract
The challenge for an owner of assets with a finite investment capacity is knowing the location, type, and magnitude of threats that impact performance. To address this challenge, the Colorado Department of Transportation (CDOT) implemented a risk-based system to quantify all geohazard and geotechnical asset risks to traveler safety, traffic disruption, and ownership costs, and how risk can change with time. The outcome measures the annual risk exposure at 0.1-mi intervals for all magnitudes and types of geohazards, including cascading events like post-wildfire debris flows blocking culverts and disrupting traffic. The recent availability of statewide lidar enabled an automated lidar data screening process to create the statewide geotechnical asset and geohazard inventory. Subsequently, risk algorithms are applied for (1) rockfall from cut slopes and natural slopes, (2) debris flows, (3) embankments and steep downhill slopes, and (4) slow-moving landslides originating off right-of-way. The plan quantifies the risk exposure at approximately 6,000 channels with a debris flow potential, 12,500 embankments with an aggregated length of 1,200 mi, and 4,100 rock cuts with a total inventory length of 270 mi.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".