A network-level management system to mitigate the global warming potential of road pavements
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
This study details a new, network-level optimization tool aimed at supporting transportation agencies in their efforts to reduce the global warming potential of their road pavement infrastructure. Through a two-stage bottom-up algorithm that integrates with a comprehensive cradle-to-grave life cycle assessment, the proposed tool learns optimal management policies for individual pavement sections and uses that information to guide network-level allocation choices. Through a realistic case study based on data made available by a state department of transportation, this study demonstrates that the proposed modelling approach identifies management strategies expected to reduce the global warming potential of a pavement network by up to 4.8% over 20 years relative to a more traditional, reactive management approach. The resulting model presented in this paper can support agencies in achieving ambitious targets to reduce the global warming potential of their paved infrastructure systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it