The review of sub-sahara africa gravel roads maintenance management system’s monitoring and need assessment: tanzania experience
Bibliographic record
Abstract
Although the urge to construct new gravel roads to reach areas, which are potential economically is still there, it is now becoming clear to the road agencies in sub-Saharan Africa that a large proportion of the gravel roads constructed or rehabilitated between 2005 and 2008 years are no longer economical.These roads have reached their terminal stage due to various reasons, ranging from improper monitoring and maintenance needs assessment to challenges of getting the required funds for gravel roads conservation.Based on the above challenges, the sub-Sahara African countries, including Tanzania, are unceasingly searching for ways to strengthen the management of existing gravel roads network as part of roads transport infrastructure.To accomplish the above demand, Tanzania has two organisations concerned with managing its gravel road networks, namely Tanzania Road Agency (TANROADS) and Local Government Authority (LGA)'s District Engineer's offi ce.Each one of these two organisations uses its own road management system.TANROADS uses Road Maintenance Management Systems, and LGA uses District Roads Management Systems.These two systems have been developed through foreign aid in terms of experts and fi nancial assistance with minimal participation of local experts.This paper focuses on factors affecting the effi ciency and effectiveness of these management systems in gravel roads monitoring and maintenance needs assessment in comparison with those in developed countries.The authors expect that by highlighting those elements affecting the provision of accurate gravel roads inventory and road condition data will improve further the diagnosis of distresses infl uencing the performance of gravel roads, and come up with proper remedy to suit the local condition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".