Analisi dei sistemi di gestione della manutenzione stradale in ambito internazionale
Bibliographic record
Abstract
[EN] In these times of crisis that countries such as Spain or Italy are living, the management and maintenance of infrastructure has become a current matter. In our work, we will focus on the management and conservation of the roads infrastructure, focusing on the management of the pavements. Also, we expose which are the most common defects in the flexible pavements and how are measured these defects and features. In addition, in this work we describe the tool of the PMS (Pavement Manager System), tool that is used nowadays for the management of the pavements of the roads network. In this thesis, we also attempt to explain how the different countries (international and European) manage the maintenance of their road networks, focusing on the management of the road maintenance, the system that is used to establish what measure adopt and in which moment and in the funding of these maintenance programs. This analysis of these has been focused on the following countries: Australia, Canada, New Zealand, United States, Italy, Germany, United Kingdom, Switzerland and Spain. Moreover, to expand the analysis to the rest of the world we have made a questionnaire which is intended to show us the main aspects of the management of the road pavement. To finish this thesis, we have made an analysis of a practical case in order to see in a real road all the application of these methods.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".