Terrain Analysis of Biu Plateau, for Road Transport Development, Borno State, Nigeria
Bibliographic record
Abstract
Among all the means of transportation, road has been described as the most important, probably because of its flexibility and its low cost in terms of construction, maintenance and usage. However, in Nigeria, road is considered to be the most dangerous means of transportation because of their bad nature such as sharp bends, narrow bridges, steep slopes and other related problems which are associated with the terrain where these roads are constructed. Road transportation therefore needs proper planning and development through the use of geo-information technologies that would ease accessibility reduces human energy and yet brings reliable and accurate information on the terrain. In this paper, Ilwis 3.5 was used to create Digital Elevation Modelling (DEM), Shadowing, 3-Dimentional View, Slope maps and river direction maps of Biu plateau to analyze the use of GIS on road planning and development on the plateau. It was revealed that the technique has great capabilities of terrain analysis as features which are deemed humanly impossible to assess are viewed as if one is at the scene which may enhance quick analysis on road transportation. It was therefore, recommended that all the stake holders in road transportation should employ the use of this geo-information techniques in terrain analysis to ease transport planning and development in the area.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".