Field Inspection Module for Hydrotechnical Hazards
Bibliographic record
Abstract
Terasen Pipelines (Terasen) owns and operates an 1146 km low vapour pressure petroleum products pipeline between Edmonton, Alberta and Burnaby, British Columbia. Its right-of-way passes through some of the most geotechnically, hydrotechnically, and environmentally challenging terrain in Western Canada. This paper describes the latest advancement of a natural hazards and risk management database application that has supported a 6-year hazard management program to quantitatively assess and prioritize the geotechnical and hydrotechnical risk along the pipeline. This database was first reported at IPC 2002 in a paper entitled “Natural hazard database application — A tool for pipeline decision makers” [1]. This second paper describes the advancements since then, including the addition of the Hydrotechnical Field Inspection Module (FIM), an add-on tool that allows field inspection observations to adjust hazard and vulnerability. This paper discusses the challenges in building a methodology that is practical enough for field maintenance personnel to use yet sufficiently comprehensive to accurately describe improving or worsening hydrotechnical hazard conditions. Functionality to enter hazard inspection data, review inspection results in the office, and authorize changes to the hydrotechnical hazard probabilities are described in the paper and demonstrated in the conference presentation. The relationship between revised hazard, vulnerability, risk, and response thresholds (such as inspection frequency, monitoring, site surveys, or mitigation) are demonstrated using a river crossing with a dynamic hazard history. As in previous years, this paper is targeted to pipeline managers who are seeking a systematic hazard and risk management approach for their natural hazards.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".