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Record W3048152658 · doi:10.1061/9780784483213.012

Utility Coordination in Alternative Delivery Methods for Transportation Projects: Lessons Learned from In-Market Design Phase (Bid Process)

2020· article· en· W3048152658 on OpenAlexaffabout
Juan Camilo Barrera, Tomasz Bodera

Bibliographic record

VenuePipelines 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsIBI Group (Canada)
Fundersnot available
KeywordsScheduleRelocationProcess (computing)Key (lock)General partnershipRisk analysis (engineering)Process managementTask (project management)BusinessComputer scienceCritical success factorPhase (matter)Operations researchComputer securityEngineeringFinanceSystems engineering

Abstract

fetched live from OpenAlex

In large linear infrastructure projects under public private partnership (P3), one of the principal risk contributors are the subsurface utilities impacted that need to be either protected or relocated. In fact, utility relocation may be a significant factor in selecting a preferred construction methods or even dictating changes on design disciplines. The P3 model is designed share risk between private and public entities. This alternate delivery approaches have an accelerated nature with aggressive schedules, where minor utility conflicts can result in significant costs and schedule impacts. The utility coordination task is a key driver and sets the critical path on the schedule. Therefore, early engagement of utility agencies in the project will be crucial, even from the initial planning phase of the project. The use of Transportation Association of Canada (TAC) guidelines and utility relocation procedures are the key elements to a successful project win and delivery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.085
GPT teacher head0.370
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2020
Admission routes2
Has abstractyes

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