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Record W4281647255 · doi:10.18280/ijsdp.170330

Conceptual Framework Design to Select Optimal Project Delivery System and Contract Strategy

2022· article· en· W4281647255 on OpenAlexvenueno aff
Atheer M. Al-Saady, Sedqi Esmaeel Rezouki

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated project deliveryProcess managementViewpointsComputer scienceProcess (computing)Plan (archaeology)Quality (philosophy)Project managementDesign–buildConceptual frameworkEngineering managementManagement scienceSystems engineeringKnowledge managementEngineeringCivil engineering

Abstract

fetched live from OpenAlex

One of the most critical factors in the implementation of wastewater projects is the development of a plan for the implementation of the project as well as the optimal project delivery method and contract strategy that suits it, which may affect the success of the project. This research aims to explore the joint planning between the project delivery system and contract strategy, with the aim of providing a conceptual framework for designing an appropriate project delivery method and contract strategy. The researcher identified seven basic delivery methods based on a review of previous studies and a questionnaire that were conducted with a group of experts working in sewage projects in Wasit Governorate / Iraq. This research designs PDCS through the analytical hierarchical process This technique has been used for several reasons, including the ease of application and its ability to identify any quality problem, as well as allowing diversity between viewpoints and its ability to bring different opinions together. It can also be applied with many applications such as linear programming and targeted programming, where the main and sub-factors that affect the choice of PDCS are studied. The design process is divided into two stages, the preliminarily design and the detailed design.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.058
GPT teacher head0.290
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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