MétaCan
Menu
Back to cohort
Record W2321187393 · doi:10.1061/40558(2001)50

Decision Support Analysis in Ontario's New Bridge Management System

2001· article· en· W2321187393 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Bridge (graph theory)Computer scienceProcess (computing)Work (physics)Benchmark (surveying)Decision support systemCost estimateOperations researchPrioritizationSoftwareRisk analysis (engineering)Process managementSystems engineeringEngineeringData miningBusiness

Abstract

fetched live from OpenAlex

OBMS employs an analyst-in-the-loop process of project evaluation and decision support. The software, written in Visual Basic, is a collection of interacting objects representing network-level concerns, such as budgets and programs, and project-level concerns, such as element-level treatment alternatives and bridge-level projects. Whenever a process such as bridge inspection or budget analysis changes any of the input data of the objects, the objects cooperate with each other to update their status. The overall result may be changes in priorities, performance, or costs. Each of the objects performs a part of a life cycle cost analysis for prioritization and evaluation of projects and policies. Projects, for example, examine their own element-level scope of work to determine the quantities of work items that will be required, and then use the Ministry's tender item cost database to estimate a project cost. Projects contribute this information to Programs, which accumulate systemwide costs and performance measures. The scope and benefit of each Project are determined at the element level. Each element is responsible for a life-cycle cost analysis, using typical benchmark costs and a Markovian deterioration model, to estimate the benefits of bridge element preservation and to select the optimal treatment for a given set of conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2001
Admission routes1
Has abstractyes

Explore more

Same topicInfrastructure Maintenance and MonitoringFrench-language works237,207