Analysis of the Major Causes of Poor Quality As-built Records of Underground Utilities
Bibliographic record
Abstract
In many cities, the underground beneath public roads is intricate with heterogeneous utilities. The situation gets worse where industrial sites are adjacent to residential areas and consequently utilities for industrial purposes and those for the daily life of people are intertwined. In striking contrast, as-built records of underground utilities are often inaccurate and unreliable so that the word “as-built” somehow loses its meaning. The lack of the actual spatial positioning information of various utilities makes it very difficult for road authorities to manage the installation and operation of various utilities beneath public roads and to manage their own road works and services as well. Poor as-built records also affect the performance and profitability of utility companies whose financial success depends on their ability to place facilities and provide services to customers in a timely and cost-effective way, which to some extent depends on the availability of accurate as-built records. This study investigates the main causes of poor as-build records of underground utilities with an aim to shed some insight on what appropriate policies can be established on the side of the government and what workable codes of practice can be implanted on the side of utilities companies such that the quality of as-built records can be efficiently improved by the joint efforts of government and industry. Accurate as-built information will play an irreplaceable role in urban planning, project design and construction, utilities operation and management, and ensuring order and efficiency in underground space utilization.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".