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
Transportation asset management systems are concerned with the daunting task of maintenance and upgrade of infrastructure within the restrictions of an annual budget. Consideration of environmental impacts is normally left out of the analysis. This paper considers the incorporation into strategic planning of environmental impacts resulting from maintenance and rehabilitation of pavements. The energy use of such activities and resulting greenhouse gas (GHG) emissions are explicitly considered, and the results of a performance-based optimization are discussed. The study followed a three-step trade-off process: (a) finding the minimum requirement for the annual budget, (b) maximizing pavement condition, and (c) reducing environmental impacts. The results showed that considering environmental impacts in the strategic planning process returned a substantial gain in energy savings and reduction of GHG emissions, although a small sacrifice in pavement performance was required. The consideration of environmental impacts reduced energy use and GHG emissions by 19% and 24%, respectively, but pavement condition dropped slightly to 98.5% of the optimal solution.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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