Quality Management System for a Highway Megaproject
Bibliographic record
Abstract
Although the concepts of quality management have been successfully applied in many industries, primarily manufacturing, and are equally applicable to the construction industry, highway megaprojects, especially those delivered through private–public partnership (P3) arrangements, present new challenges. The current movement from the use of traditional method specifications to the use of end-result specifications and the transfer of responsibilities for quality to the developer and builder give rise to the need to verify quality performance. A quality information management system becomes a necessity given the volume of information generated. The challenges of developing and implementing a quality information management system for a highway megaproject in New Brunswick, Canada, are discussed. The system developed addresses key needs including support for various levels of management (technical and executive), an open data structure and interoperability, hierarchical information levels, integration with facilitywide management information, and future scalability. The system developed addresses the needs of quality information management for a private project developer but can also be adapted to other project delivery mechanisms. The tool was implemented to support the entire project team, including construction field supervisors and the project’s senior management. A documented analysis is offered of a generic implementation process that can be adopted in other projects to improve efficiency in quality information management in the highway construction industry in general and in megaprojects delivered through P3 arrangements in particular.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".