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Record W2620584845 · doi:10.1061/9780784480724.026

Communication and Management of Geotechnical Risks to Transportation Performance Objectives

2017· article· en· W2620584845 on OpenAlexaff
Scott A. Anderson, Mark Vessely, Ty Ortiz

Bibliographic record

VenueGeo-Risk 2017 · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsRisk managementGeotechnical engineeringComputer scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.510
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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