Unsupervised Neural Network for Data-Driven Corrosion Detection of a Mining Pipeline
Why this work is in the frame
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Bibliographic record
Abstract
Pipelines failure often caused by corrosion may result in safety, environmental and economic issues. In this study, an unsupervised neural network, Self-Organizing Maps (SOM), is applied to create clusters representing the corrosion impact assessed with ultrasound periodic inspections. Based on this work, it is expected that the new insight into thickness data representation using unsupervised neural network will facilitate planning of corrosion mitigation activities through risk-based inspections of mining slurry pipelines. As a result, SOM led to the reduction of the variables in two-dimensional space nodes. Hierarchical ascending classification (HAC) was then used to classify these nodes regrouping thickness loss measurements. The proposed method by combining both SOM and HAC succeeded in detecting the extent of corrosion in a mining pipeline.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it