4D Inspection: A Comprehensive Platform to Digitize Pipeline Construction Inspection and Generate Data Driven Continuous Improvement
Bibliographic record
Abstract
Abstract Beginning in 2018, TC Energy began an effort to digitize inspection and construction management and internally named this concept the Dynamic, Digital, Data and Diagnostics platform otherwise known as “4D Inspection”. The 4D Inspection platform is built upon Project Consulting Service’s Epilogue® energy infrastructure construction management solution and is intended to evolve inspection reporting to a digital platform to create standardized construction reporting embedded with real-time compliance validations for efficient and effective management of field construction issues, progress tracking, and over-all construction quality monitoring. Currently, this effort is nearing the end of its first year of a multi-year implementation plan, the platform is in use on projects in both Canada and the United States spanning four different time zones with more than 595 inspectors on over $7.5 billion in capital projects. Even with implementation still underway, this concept’s key functionality, like automated inspection report document control, simplified photo capture with geo-tagging and automated daily progress reporting, has provided immediate benefits of more thorough, more reliable, and more efficient construction data than ever gathered using previous data collection processes. With improved data accuracy and detail, TC Energy gains insight and even foresight into how best to advance construction quality and safety while also impacting overall project costs and schedule.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".