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Record W6980718204

Construction Management Practices in Canada and Europe

2005· other· en· W6980718204 on OpenAlexaboutno aff

Bibliographic record

VenueRosa P: A digital library for transportation research (United States Department of Transportation) · 2005
Typeother
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
FundersFederal Highway AdministrationU.S. Department of Transportation
KeywordsNucleofectionTSG101DiafiltrationLiquationGestational periodArticular cartilage damageProteogenomicsHyporeflexia
DOInot available

Abstract

fetched live from OpenAlex

Construction management is an essential element of transportation project success, and evolving industry roles are creating changes in conventional U.S. construction management practices. The Federal Highway Administration, American Association of State Highway and Transportation Officials, and National Cooperative Highway Research Program sponsored a scanning study of construction management practices used in Canada and Europe for effective project delivery, contract compliance, and quality assurance. The U.S. team observed that the Canadian, European, and U.S. transportation communities face similar political, financial, and resource challenges, but Canadian and European agencies have developed construction management systems that promote more collaboration between the public and private sectors and create stronger long-term partnerships. The international agencies are more willing to delegate traditional highway functions to the private sector when cost and schedule benefits are significant. The team’s recommendations for possible implementation in the United States include developing risk assessment and allocation techniques, using qualifications in procurement, piloting early contractor involvement, applying alternate bids and designs in procurement, conducting preproposal meetings, and using appropriate alternative payment methods.\n

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.390
Teacher spread0.333 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2005
Admission routes1
Has abstractyes

Explore more

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