Pavement-Distress Data Collection System Based on Mobile Geographic Information System
Bibliographic record
Abstract
In recent years, the development of geographical information systems (GIS) has shifted significantly from desktop applications to mobile field applications. Integrating the precision location data collection capability of a Global Positioning System (GPS) and the spatial processing power of mobile GIS provides an adequate system for field workers to collect data with increased efficiency and ease. This research aims to design and implement a pavement-distress data collection prototype for inspecting airport pavement condition, with the aid of mobile GIS. Unlike the traditional paper forms, the data collection system seeks to offer a customized user interface for distress data entry and a spatial query service. Initial preparation of the airport map layers was carried out on desktop computer using a variety of off-the-shelf software. The required files were then transferred to the personal digital assistant (PDA) for field trials. Two other handheld devices, namely the GPS receiver and digital camera, are attached to the PDA to capture precise location coordinates and digital photos, respectively, of the identified distress. The users are also able to obtain comprehensive search results of the distresses, in both an attribute table and map visualization form. To increase the search efficiency, an indexing method was implemented for the data collected. Trial experimental results show that the applied indexing method has significantly improved the search efficiency over the conventional exhaustive search method.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".