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Record W636800691

PAVEMENT TECHNOLOGY FOR MEGA TRANSPORTATION PROJECTS

2001· article· en· W636800691 on OpenAlexaboutno aff
J Emery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLife Cycle Costing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTransport engineeringLife cycle costingTollEngineeringActivity-based costingFlexibility (engineering)Track (disk drive)Civil engineeringBusiness
DOInot available

Abstract

fetched live from OpenAlex

The demonstrated durability, flexibility and structural capacity of hot-mix asphalt and PCC concrete makes it possible to provide economic, smooth, safe pavements for highways and airports for heavy traffic conditions and severe operating environments. A mega transportation project is considered to involve: greater than $100 million in construction costs; an alternative delivery method such as design-build; life cycle costing for technology evaluation; fast track construction; and total quality management. Recent involvement in mega projects - Dominican Republic Duarte Highway, Colombia Bogota El Dorado International Airport, Highway 407 ETR-Express Toll route north of Toronto, Nova Scotia Cobequid Pass Highway and New Brunswick Fredericton-Moncton Highway - is used to illustrate the advantageous application of asphalt and concrete technology, with emphasis on life-cycle costing and value engineering. The focus of the pavement technology described is on the practical concepts involved, and particularly the key role of the paving contractor in meeting the fast-track technical and quality requirements of mega paving projects. For the covering abstract of this conference see ITRD number E201066. (A)

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.028
GPT teacher head0.242
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2001
Admission routes1
Has abstractyes

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