Evaluation of Automated Distress Collection Techniques: An Ontario Case Study
Bibliographic record
Abstract
Pavement management systems (PMS) rely on consistent and repeatable distress data collection. Traditionally, such data has been collected through manual surveys, which are subjective, tedious and time consuming. Ideally, the data would be collected at travel or high speed, using state-of-the-art image capture equipment. The Ministry of Transportation of Ontario (MTO) has initiated a study with the University of Waterloo to determine which of these units or systems, if any, are applicable to Ontario needs and if so whether they can replace the existing manual approach. The work plan has involved a literature review, progressing to an identification of the most promising technologies and then the design and execution of a field experiment to compare and assess the automation technologies vis a vis the manual method. Overall, the results from this study indicate that there are no significant differences among contractors' measurements using sensor-based equipment; however, there are significant differences among measurements taken using digital image-based technology. The implications of such outcomes are discussed in detail, including the specifics regarding methodology implementation in order to encourage practitioners to benefit from the preliminary investigation. In a broader perspective, this paper provides an opportunity for road agencies to revisit selection decisions concerning the acceptance or rejection of pavement data collected by a wide range of contractors.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".