Backscatter Computed Tomography (BCT) Pilot Project for Culvert Integrity Analysis in the City of Toronto
Bibliographic record
Abstract
Infrastructure owners must decide between rehabilitating or replacing aging assets, however, appropriate asset health information is simply not available for certain structures. An asset class identified by The Ontario Good Roads Association (OGRA) that presently lacks health information is major roadway pipes (culverts). A critical culvert failure mode is due to the formation of or voids behind the culvert wall caused by soil erosion. The level of undermining is currently not measured when evaluating replacement or rehabilitation possibilities. This information gap is of particular concern for planning slip-lining or other rehabilitation projects since significant compromises the culvert's fundamental structural integrity. Lack of complete integrity information can lead to unnecessary and costly replacements, and poses a risk to public safety. During the summer of 2011, OGRA supported an integrity analysis pilot project on selected roadway culverts using recently-developed inspection equipment capable of quantifying undermining. This new modality, Backscatter Computed Tomography (BCT), is a practical in-situ form of Computed Tomography also known as a CAT Scan. The purpose of the pilot was to evaluate the technology's potential to measure undermining, thereby facilitating the planning of major roadway culvert replacements or rehabilitation. Three culverts in the City of Toronto were inspected to determine the culverts' structural condition using BCT equipment provided by Inversa Systems Ltd. (A) For the covering abstract of this conference see ITRD number 201211RT334E.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".