Communication and Management of Geotechnical Risks to Transportation Performance Objectives
Bibliographic record
Abstract
The Colorado Department of Transportation (CDOT) has recently implemented a risk-based transportation asset management plan (RB TAMP) that incorporates geotechnical assets and hazards. CDOT’s RB TAMP includes an ancillary wall structures program that includes all earth retaining structures, and a geohazards management program which is used to manage multiple hazards related to slopes, embankments, and roadway subgrade. The RB TAMP states multiple performance goals to be achieved, including safety, infrastructure condition, reliability, congestion, and maintenance, and the state will measure and report progress in these areas. Natural hazards, physical failures, external agency impacts and operational risks are risk types that present threats to CDOT’s achievement of their goals. The way these risks act on assets to impact performance goals can be visualized in a cubic form, and this allows for recognition of how many elements of risk there are, for making explicit decisions on which risks to address and how, and for communicating these decisions to others. Risk analysis at CDOT includes both qualitative and quantitative approaches in accordance with data availability. The quantitative estimate of risk is expressed in terms of exposure cost for all assets, risk types and performance goals and then used by CDOT subject matter experts for project selection and planning.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".