Evolving Excavation Damage Modelling Predictions to a Data-Driven Approach
Bibliographic record
Abstract
Abstract The evolution of regulatory codes, standards, and practices has increased the need for risk modelling to become data-driven to support more informed decision making. One Call1 information is a source of regular insight into activities along the pipeline right of way that could influence excavation damage prediction algorithms. However, the treatment of One Call data related to threat impact is challenging. Advances in technology, analysis techniques, and quantity of data available and accessible now afford for data integration and the observation of meaningful outcomes regarding relevance of One Call data to leak and repairs based on related design and geospatial data elements. This paper will review the current challenges faced in excavation damage modelling used in pipeline risk algorithms, present alternatives for increasing granularity and value in the risk assessment, discuss the uncertainties involved with leveraging these alternatives, and share remaining work required to improve the accuracy, prediction, and reliability of excavation damage prediction algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".