Field Trial of the NoPig Inspection System
Bibliographic record
Abstract
The NoPig system is an above ground metal-loss detection tool utilizing magnetics. Sensors at ground level detect disturbances in the magnetic field around the pipeline generated by impressed alternating current (AC) signals. This tool is intended for use on segments of pipeline which are considered unpiggable. Previous field trials indicated the tool was capable of detecting metal-loss in small diameter seamless pipe. Trials on electric resistance weld (ERW) or double submerged arc weld (DSAW) pipe were inconclusive. Modifications have been made to the NoPig hardware and analysis software to correct for the non-uniform magnetic fields produced by seamed pipe and girth welds. The study reported in this paper is a field trial of the modified inspection system. Recently inline inspected pipelines of nominal pipe size (NPS) 12 and 16 were selected for survey. Pipeline segments where significant metal-loss was detected from Inline Inspection (ILI) were selected for the blind test. Eight hundred meter sections of pipeline were surveyed at each of these locations to ensure a range of pipe conditions were included. After all surveys were complete, significant features were excavated and actual measurements were obtained. This paper describes the field inspection program as well as the analysis process used to verify the detection capabilities of the modified NoPig system. The results will include discussion of the positional accuracy, detection capability and threshold of the system. This analysis will help determine if the NoPig system is suitable alternative for assessing the integrity of unpiggable pipeline segments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".