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Record W374085433

Building a sustainable future : proceedings of the 2009 Construction Research Congress, April 5-7, 2009, Seattle, Washington

2009· book· en· W374085433 on OpenAlexaboutno aff
Samuel T. Ariaratnam, Eddy M. Rojas

Bibliographic record

VenueAmerican Society of Civil Engineers eBooks · 2009
Typebook
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsChinaProcurementEngineeringConstruction managementProject managementConstruction industryEnvironmental planningPolitical scienceBusinessCivil engineeringGeographyConstruction engineering
DOInot available

Abstract

fetched live from OpenAlex

Construction Research Congress 2009 contains 152 peer-reviewed papers presented at the conference held in Seattle, Washington, April 5-7, 2009. Sixteen countries were represented: Australia, Brazil, Canada, China, Colombia, Hong Kong, India, Israel, Korea, Japan, Saudi Arabia, Singapore, South Africa, Thailand, Turkey, and the United Kingdom. The papers are organized into general state-of-knowledge research areas in construction engineering and management. Topics discussed include: project control, general Operations issues, procurement and contracting, organizational leadership and management, advances in project planning, design, and construction, sustainable construction and facilities, project risks and safety, project planning and control, project/process integration and improvement, lean construction, quantitative methods and models, infrastructure management and disaster mitigation, underground construction, construction simulation, education, and research methods. This proceedings is invaluable to construction engineers and researchers, contractors, and others involved in the field of construction.

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0960.038

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 head0.312
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 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
Published2009
Admission routes1
Has abstractyes

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