The Challenges of Mobility Within Owerri City, Nigeria
Bibliographic record
Abstract
The study identifies the road transportation problems in Owerri city, Nigeria and determines whether the existing road network in Owerri city, is adequate for effective worktrip. This is necessary because for many years the city of Owerri has faced a lot of road transportation problems, which has created many social, physical, economic and political tensions in the city. The survey method using questionnaire was employed in the study.The stratified, random and systematic sampling techniques were used in selection of eight routes and 240 respondents from six zones of the city. The result showed that the worst transport problem experienced by the inhabitants of the city is traffic congestion. The study therefore concludes that the contribution of the state government and the municipal authority in improving transport problems should come in the form of developing more road networks, increasing mass-transit buses, encouraging the use of bicycles and providing pedestrian walk ways.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".