Natural Hazard Database Application: A Tool for Pipeline Decision Makers
Bibliographic record
Abstract
Trans Mountain Pipe Line Company Ltd. (TMPL) owns and operates an 1146 km NPS 24 low vapor pressure petroleum products pipeline between Edmonton, Alberta and Burnaby, British Columbia. In 1998 TMPL retained BGC Engineering Inc. (BGC) to start a three-phase geotechnical and hydrotechnical hazard assessment of the right of way (ROW) from Hinton, Alberta to Kamloops, British Columbia. As part of this work GroundControl was asked to develop an electronic database with which to capture the information generated by BGC during the hazard assessment work. This paper describes the development and evolution of the database application that accompanied the study to quantitatively assess and prioritize the geotechnical and hydrotechnical hazard potential along the pipeline. This paper describes how the database provides TMPL employees across British Columbia and Alberta access to the current results of the hazard assessment plus supporting information such as multi-temporal images and internal and 3rd party reports about the pipeline. The purpose of the database and the unique architecture and functionality that accommodates ongoing monitoring and inspections of slopes and stream crossings is provided. Database security, access, and information sharing unique to TMPL are also described. Benefits and costs of the application plus technical and business challenges overcome by TMPL, BGC, and GroundControl are discussed. Recommendations from TMPL and GroundControl for similar information management initiatives are provided and future work is described. This paper is targeted to pipeline managers who are looking for economical, practical, and innovative information management solutions for managing their natural hazards.
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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".