Evaluation of the positional accuracy of subsurface utilities
Bibliographic record
Abstract
Since WWII, the urban underworlds have become a web of utility lines, including telecommunication lines, buried electricity lines, gas mains, watermains, cable TV, fiber optic cables, street lighting, and storm and sanitary sewers. From preliminary design stages to breaking ground on new construction projects; owners, designers, engineers, and contractors rely on existing underground utility records as an initial source of information. There is a constant need for underground utility information and most of the city's existing utility records are not only irretrievable, but are also out-of-date. According to research done in the past, records and visible feature surveys by site are a significant percentage off the mark and, in some cases, considerably worse. This study focuses on the evaluation of the positional accuracy of subsurface utilities within seven projects, within the City of Toronto, using an offset approach. It also aims to reveal the magnitude of the problem surrounding the obtainment, analyzation, and interpretation of information with respect to underground infrastructure facilities. None of the projects show any relationship or correlation with positional accuracy and the factors that are thought to affect the accuracy of underground utility information (e.g. type of soil, type of utility, date of installation, right-of-way, etc.). The analysis indicates a clear indication of no systematic patterns between the right-of-way parameters and utility type parameters. Based on the results of this study it can be stated that the process of obtaining subsurface utility information is still a time-consuming, inefficient, costly, and difficult process.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".