Underground Utility Mapping using a Portable Sensor System
Bibliographic record
Abstract
A common concern in most governments is the renewal of infrastructure. With the aging state of the infrastructure in virtually all countries, there is a growing need to optimize the maintenance process due to the associated high capital costs. In addition, there is a recognized need for precise three-dimensional information related to the underground infrastructure due the potential hazard related to gas line strikes during construction. Unfortunately, the tragic results of many incidents have been documented over the last few years. A report from North Carolina State University states that there is one death per day (globally) due to punctured sub-surface utilities. Accurate infrastructure information will improve both municipal infrastructure renewal plans and worker and public safety on infrastructure construction sites. Case studies show that infrastructure information is often not up-to-date, or reliable, which can prohibit municipal decisions from being cost effective. By increasing the quality of the information, the confidence of infrastructure contractors will increase, and the risk of adversely contacting an underground asset will decrease, thereby elevating worker and public safety. A practical solution to this problem is the development and implementation of a mobile terrestrial photogrammetric mapping system to map exposed utilities on construction sites. This paper outlines the design of the Underground Infrastructure Mapping System (UIMS). The UIMS was designed to accurately map exposed underground utilities, and generate geospatial information for municipal underground utility databases; containing all of the necessary information to perform underground asset management. The system is comprised of three hardware components including a tablet PC, a Global
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".