Applying Geographic Information Systems (GIS) for surface condition indicators modeling of a flexible pavement
Bibliographic record
Abstract
In Morocco, as in all world countries, deteriorating road conditions, increasing traffic loads, and decreasing funds have presented a complex management challenge for the road maintenance and rehabilitation process. Hence the need to assess the condition of the pavement network, decide on maintenance strategies, set rehabilitation priorities and implement a maintenance management system. In this regard, Geographic Information System (GIS) is a powerful tool for managing and analyzing data referenced to a geographic location, especially in the field of road infrastructure where information on pavement sections stored in textual databases can be linked by location and attribute in geographical maps. This paper presents a case study, explores the ability of a GIS to visualize the different levels of surface degradation of flexible pavement through the analysis of GIS surface indicator matrices and the reduction of road databases containing the results of the environmental inspection carried out in 2018 on a 50 km section of the Moroccan national road number 06 from KP 0+080 to KP 0+130, applying the Moroccan method carried out by the Moroccan National Center for Road Studies and Research. These thematic maps can justify a budgetary request for the investment of public funds and help in maintenance decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".