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Record W599160489

Louisiana Highway Construction Cost Trends After Hurricanes Katrina and Rita

2008· article· en· W599160489 on OpenAlexaboutno aff
Guangxiang Cheng, Chester G. Wilmot

Bibliographic record

VenueTransportation Research Board 87th Annual MeetingTransportation Research Board · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsHurricane katrinaIndex (typography)Quarter (Canadian coin)Transport engineeringAgency (philosophy)Cost estimateNatural disasterEngineeringGeographyCivil engineeringEnvironmental scienceMeteorologyComputer scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The object of this study is to reveal the highway construction cost trend after hurricane Katrina and Rita in Louisiana. The method to reveal the impact is to build a Louisiana Highway Construction Index (LHCI). This index is made up with the cost of construction material, labor and equipment of 6 major material categories in highway construction. The data is from projects let in the Louisiana Department of Transportation and Development from 2003 to the 2nd quarter of 2007. The indices were built for statewide, hurricane impacted areas (GO Zone) and non-GO Zones. The indices revealed that two quarters after hurricane Katrina and Rita, the highway construction cost jumped about 20% statewide and 51% in GO Zone. Two years after the hurricanes, the cost has stabilized to around 30% increase over the pre Katrina and Rita period. This study provides valuable information for the state agency to estimate cost escalation in on-going projects and to estimate future disaster response to highway construction costs. Keywords: Highway construction cost, index, hurricane

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.122
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.134
GPT teacher head0.423
Teacher spread0.289 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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