APPLICATION OF AN INFORMATION AND KNOWLEDGE MANAGEMENT METHODOLOGY IN ANALYZING THE RISKS IN CONSTRUCTION PROJECTS
Bibliographic record
Abstract
The multitude and diversity of risks encountered on infrastructure projects necessitates an approach to manage a significantly large body of information regarding risks and their properties. Computer-based methodologies that make use of advances in Information Technology (IT) have the potential to play a significant role in facilitating the management of this body of information and also in assisting the capture of knowledge gained on projects in a manner suitable for re-use in the future. In this paper we describe the development of a methodology for information and Knowledge application and re-use in RISk management (KRIS) and its application towards the analysis of risks on a case study building project proposed for construction in the Greater Vancouver Regional District (GVRD). The case study is a unique one-off facility that involves multiple public and private sector stakeholders and a complex program to accommodate over 1800 employees. This case study is used to illustrate concepts addressed in KRIS and in particular how this IT application can assist project personnel address risks in a complicated project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".