Entwicklung von Systemelementen für ein österreichisches Pavement Management System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Development of System-Elements for the Austrian Pavement Management SystemThis PhD-The sis contains the foundations for the practical use of an executable pavement management system on the Austrian federal road network.This system, which is enlisted for decisions and evaluations on network level, uses an optimization tool for calculating the most efficient maintenance strategies, including performance prediction models and cost models.Besides a detailed description of each single element, such as data-collection, datastoring, performance prediction of road condition, and data analysis, the main focus of this thesis aims at deriving indexes to describe pavement construction, traffic-load, and road condition.Beyond it, the development of a treatment catalogue enables a description of monetary and non-monetary impacts of a measure on pavement condition.The formulation of different sub-targets for road maintenance (safety, comfort, and structural maintenance) allows to determine a total-condition-index, which is put together from single attributes.On the one hand this index can be used to define the benefit of maintenance treatments and on the other hand it can be used for optimization.Ultimately, the evaluation of the results is a combined method of cost-benefit-analysis and heuristic optimization.For that purpose a special database, named VIABASE , and a computer-assisted management system, named VIAPMS , of Canadian origin are used.These software tools are used by the road administration authorities too.The results of calculation can be enlisted for political decisions on network level but also for further evaluations to create a construction program or working program respectively by the road administration authorities.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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 it