Conceptual Framework Design to Select Optimal Project Delivery System and Contract Strategy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".