Advanced Multi-Sensor Inspection Critical Condition Assessment on Wastewater Infrastructure
Bibliographic record
Abstract
SewerVUE Technology (SewerVUE) was subcontracted by Whissell Contracting and the city of Calgary to inspect two critical sanitary trunks located under Memorial Drive in Calgary, Alberta, Canada. Measurements of sediment levels, structural defects, and wall thicknesses of approximately 600 linear meters of 1,200 mm I.D. and 1,950 mm I.D. R.C.P. were taken. An advanced multi-sensor robotic float equipped with HD closed circuit television (CCTV), sonar, and 3D Light Detection and Ranging (LiDAR) was used. Quantifications of the staining and sediment deposits throughout the pipe were reported along with joints and wall loss. Equipped with this data, the city of Calgary and its engineers can make evidence-based decisions regarding the pipes’ rehabilitative needs. This case study demonstrates the use of advanced pipe condition assessment technologies as a cost-effective, non-destructive means to refine the estimated remaining useful life (RUL) of an interceptor, accurately determine the overall severity of pipe degradation, and provide a basis for improved cost allocation or timing of rehabilitation efforts.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".