Measuring the Effectiveness of Damage Prevention Techniques and Defining the Key Performance Indicators on Damage Prevention Efficiency
Bibliographic record
Abstract
Pipeline operators expend substantial efforts to develop, implement, and audit their Public Awareness and Pipeline Damage Prevention Programs. While the rate of pipeline damage incidents from third-party and outside force impacts has progressively declined over a period of several decades, these events remain a high priority for the pipeline industry and external stakeholders. There are multiple management and communications tools that are used to support Damage Prevention programs for energy transmission pipeline operations. These tools are applied to large pipeline systems that cross a range of geographic, population, and regulatory boundaries. These factors make it challenging to determine the effectiveness of the individual tools applied for damage prevention for energy transmission pipeline systems. This paper present the results of research performed through Pipeline Research Council International, Inc. (PRCI) to measure and quantify the effectiveness of the various damage prevention tools and techniques as they apply to energy transmission pipeline systems. The project focuses on data collection through a web-based platform to provide the basis to establish a set of Key Performance Indicators (KPIs) for assessing the effectiveness of the methods and techniques that are used as standard practices by most pipeline operators in their damage prevention programs. The research includes development of a consistent and systematic process and database for collecting information on damage and “near hit” incidents that are recorded by pipeline operators. Fault-tree analysis of these data is expected to show where improvements can be made (e.g., one-call center, ticket handling, operator response, contractor cooperation and diligence, locating and marking, monitoring). Improvements will be measured by PRCI by capturing and analyzing the data over a multi-year period. The key output of the project will be metrics that demonstrate which damage prevention activities are more effective in reducing impacts and “near hits” to pipelines and which activities positively contribute to the safe operations of the pipeline system.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".