Evaluation of sustainability assessment tools for road construction projects applied on an asphalt road rehabilitation project in Germany
Bibliographic record
Abstract
The increasing awareness of the need for sustainable development in road construction and the growing number of different assessment tools worldwide has led to a general demand for a tool specific to the German market with its current environment and regulations. This thesis briefly reviews the overall development of the sustainability concept and then applies different tools for the evaluation of road constructions using the example of a small rehabilitation project in Germany. Further, a brief overview of the existing international and European standards and guidelines will be given, followed by a more detailed description of the Swiss method NISTRA, the application of the British asPECT, the Canadian Athena Pavement LCA and the French SEVE. Partly limited access to the tools and the small amount of available data due to the project size has led to rather limited results and it remains to be seen if a bigger project and a better access would result in a more sophisticated outcome. It can therefore be concluded that the sustainability evaluation tool for small rehabilitation projects specific to Germany would need a more simplified handling and structure then what is currently available.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".