Bibliographic record
Abstract
This paper describes the development of Pipe Penetrating Radar (PPR), the underground in-pipe application of GPR, a non-destructive testing method that can detect defects and cavities within and outside mainline diameter (>;18 in/450mm) non-metallic (concrete, PVC, HDPE, etc.) underground pipes. The method uses two or more high frequency GPR antennae carried by a robot into underground pipes. The radar data are transmitted to the surface via fibre optic cable and are recorded together with the output from CCTV (and optionally sonar and laser). Proprietary software analyzes the data and pinpoints defects or cavities within and outside the pipe. Thus the testing can identify existing pipe and pipe bedding symptoms that can be addressed to prevent catastrophic failure due to sinkhole development and can provide useful information about the remaining service life of the pipe, enabling accurate predictability of needed intervention or the timing of replacement. This reliable non-destructive testing method significantly impacts subsurface infrastructure condition based asset management by supplying previously unattainable measurable conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".