The Possibility of Building a Road Maintenance Fund Scheme to Initiate Pavement Smoothness Incentive in Taiwan
Bibliographic record
Abstract
Research from Canadian C-SHRP program has found that even a small mitigation to the initial pavement smoothness during the construction period will have a sound benefit to (1) the long term pavement performance (2) cost down of the annual maintenance cost and pavement life cycle cost. If the highway authority provides smoothness incentives to encourage contractors to improve the pavement riding quality, in the long run, the highway authority will earn more rewards from the long term pavement performance. Taiwan will finish her almost island wide expressway network system before year 2010. Due to the limitation of available resource of land and fund, the surface transportation strategies of MOTC will transfer gradually from building new roadway to the needed maintenance operations of the existing network. Due to the governmental regulations constraint, there’s still no pavement smoothness incentive scheme build yet. It’s suggested to initiate a Road Maintenance Fund (RMF) to provide the need money of smoothness incentive. Offer an upper limit of 30% of the project bid for “smoothness bonus” may encourage contractors to finish a better pavement.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".