Risk profile modeling for large scale projects : case study of a transmission line project
Bibliographic record
Abstract
Over the next several years in North America, the power grid needs to be revitalized and extended to deal with aging infrastructure, capacity constraints, and the pursuit of renewable energy sources. In Canada, and particularly the province of BC, very significant complexity and risk is involved in the approval, design and construction of such projects given highly variable terrain and weather conditions, the multiplicity of the environmental, First Nations, and third part stakeholder issues involved, and challenging regulatory and procurement processes. Described in this research is a holistic approach to the identification of risk as a function of project context, the representation of which is made difficult in the context of transmission line projects because of their large spatial scope and the vast volume of data of different types to be distilled and analyzed. Central to the approach is the representation of a project within an integrated environment in the form of multiple views of a project – product, process, participant, environment and risk. Treatment of the first four views aids the identification of risk drivers for a risk event. Knowledge of risk drivers assists with expressing likelihood of occurrence of a risk event and the magnitude of impacts should it occur, and selecting the most appropriate risk response. Application of the approach to a 255 km 500 KV design-build transmission line project is featured and challenges involved in developing its risk profile highlighted. How data visualization can assist development of a project’s risk profile and facilitating insights into it is also demonstrated. The use of the holistic approach described for the development of a project’s risk register and mining its contents using data visualization to generate useful insights has proven to be of significant value to project personnel.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".