Applying Machine Learning Techniques to Identify and Predict Behavior of Rectifier and Groundbed State Change
Bibliographic record
Abstract
Abstract We explore how rectifier voltage and current measurements can inform pipeline engineers and technicians on the health, performance and operation of their cathodic protection (CP) assets, and predict the future operation of existing and newly installed cathodic protection systems. We leverage years of data from monitoring units installed on CP rectifiers combined with site specific details describing the site and its CP system provided by pipeline operators to train a machine learning model. The study includes current and historical data from hundreds of unique rectifier locations across Canada which have been historically monitored using a remote monitoring unit (RMU). RMU readings are analyzed and grouped by long term resistance trends. Contextual data is collected for each site. This data describes the cathodic protection relevant details of the site, including details of the pipe, rectifier, groundbed and soil. A machine learning model has been developed which accepts the contextual data associated with the rectifier and will predict the long-term rectifier resistance trend.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".