On Studying the Prospective Road Construction Materials Demand for 50 Years
Bibliographic record
Abstract
Abstract Global road networks play a huge role in social development, economic growth, and easy access to natural resources. In today's world, road transportation carries more than 80% of passengers. The world’s population is expected to grow to 9.8 billion by 2050, and half of the anticipated growth in the world population will be in Africa. In the development of economic activity, the demand for people and goods transportation is a prime indicator of changing demographics. As of 2021, there are 6,958,538 million people living in Libya. According to current estimates, the Libyan population will be more than 10.8 million by 2050. The road mesh was 83,200 km in 2010. By 2050, Libya's road network is expected to increase by at least double its current length, which is 95,180 km of paved roads. In this study, the potential future paved road network is estimated, as well as the expected amount of material to be used for road construction. On typical pavement construction layers in Libya, at least 249,847,500 m3 of materials will be needed to construct new roads over the next 50 years. In addition, the country will require a significant amount of materials to cover the road maintenance and rehabilitation work required during t3hat same period.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".