U–CARE–PM: A real-time gating framework for catastrophic risk in critical utility projects
Bibliographic record
Abstract
Critical utility projects cannot pause scheduled milestones without risking severe operational and regulatory disruptions. This study introduces U–CARE–PM, a six-component framework—Real-Time Continuity Gate (RTCG), Continuity–Compliance Gauge (CCG), Log-Scaled Catastrophic Risk (LSCR) index, Regulatory Feed Engine (RFE), Resource Allocation Matrix (RAM), and an optional Early Compliance Drift Indicator (ECDI)—designed to manage high-impact, low-probability (HILP) threats under continuous operations. A multi-case study was conducted across four anonymized organizations—engineering services, infrastructure construction, IoT water-tech, and a multinational energy–water operator—over a six- to twelve-month period. U–CARE–PM was integrated into existing ERP-BPMS or SCADA systems, with tiered implementations reflecting digital maturity. Data sources included operational logs, semi-structured interviews (8–15 participants per site), and Delphi panel validation thresholds (CCG = 0.85; LSCR = 10). Pilot deployments reduced downtime by 18–24%, lowered compliance breaches by 30–40%, and improved schedule adherence by eight points. One site prevented an estimated USD 1 million outage through near-real-time resource reallocation. While the ECDI occasionally generated false positives, it accelerated regulatory response and reduced overnight emergencies. Unlike static milestone-based controls, U–CARE–PM integrates real-time continuity metrics, log-based risk weighting, and continuous compliance updates. This framework offers a replicable, data-driven approach for nonstop infrastructure projects, enhancing resilience and agility in the face of catastrophic risks. Compared with existing literature on project resilience, it advances the field by shifting from retrospective control toward proactive, continuous risk-compliance management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".