Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underground pipe inspection represents one of the last frontiers for Ground Penetrating Radar. The near ideal circumstances (low electromagnetic background environment, constant geometry of concrete pipes, reasonable required penetration depth, etc.) are more than offset by operational challenges. These include the need for a reliable apparatus to keep the antennas in constant contact with the pipe wall, a mechanism to hold them at the desired position and the means to communicate the data over long distances (>;1500 ft or 500 m) in often active sewer pipes in various states of flow. Moreover, the interpreted data has to be presented together with the CCTV output to a lay audience. These challenges have been overcome in a patent pending technology code named SewerVUE In-Pipe GPR, or Pipe Penetrating Radar (PPR). This technology significantly impacts subsurface infrastructure condition based asset management by providing previously unattainable measurable conditions. This paper will summarize the PPR technology development, current methodology, identifying assessment applications, and illustrate how PPR presents critical structural information surrounding buried non-ferrous pipes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it