MétaCan
Menu
Back to cohort
Record W2743034440 · doi:10.1061/9780784480885.011

Buried Pipeline Utility Relocation for Light Rail Transit in Phoenix What Is the Best Project Delivery Method?

2017· article· en· W2743034440 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePipelines 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsRelocationPhoenixPipeline (software)Transit (satellite)Computer scienceRail transitLight rail transitTransport engineeringEngineeringPublic transportOperating system

Abstract

fetched live from OpenAlex

Valley METRO used three delivery methods to build the first three light rail transit (LRT) segments in the Phoenix metropolitan area. Central Phoenix East Valley, completed in 2008, used a conventional Design-Bid-Build approach. However, Northwest Extension and Central Mesa Extension, which were finished in 2015 utilized a Construction Manager at Risk approach and a Design-Build approach, respectively. Utility relocation is an essential part of LRT system construction, where the alignment is located in an existing road right-of-way. The project delivery method impacts both the design and the construction for utility relocation in many ways. Three notable areas of differences are potholing, schedule and partnering. Using examples from each LRT segment, similarities and differences in impacts for each delivery method, are analyzed from a designer’s perspective.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.861

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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