PIPEiD: Pipeline Infrastructure Database
Bibliographic record
Abstract
Numerous water sector management practitioners have stated an urgent need to have a unified platform for the nation’s water pipeline infrastructure data and information that is universally accessible and useful. Such a platform, PIPEiD (pipeline infrastructure database), is envisioned to provide access to the data sources, tools, and models that enable the analysis, simulation, visualization, and evaluation of the behavior of pipeline infrastructure. PIPEiD will assist the users in more effective management of these assets for sustainability and resiliency. Virginia Tech, WERF, Washington Suburban Sanitary Commission, Denver Water, and American Water jointly sponsored three workshops to sharpen the PIPEiD (pipeline infrastructure database) vision and mission, both of which are focused on enhancing the practice of drinking water, wastewater, and stormwater pipeline asset management. With invited researchers from academia, utilities, regulators, organizations, and industry, the workshop identified opportunities and knowledge gaps relative to critical areas of sustainable and resilient pipeline infrastructure systems and other pipe associated assets. The goal of the workshop was to develop a prioritization that can guide fundamental and applied research at institutions and entities funding research in water pipeline infrastructure. A key question discussed at the workshop was “how to develop data standards, model specifications, and decision support tools for advanced pipeline asset management to allow for higher reliability and improve performance, cost-effectiveness, risk management, efficiency, sustainability, security, and resiliency.” This paper presents the workshop outcome focused on the design and development of a national database platform to allow a practitioner to address all three major water pipeline infrastructure management levels: strategic, tactical, and operational, and for water utilities of all sizes.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.078 |
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".