A Spatio-Topographical Assessment of Mobile Network Quality in Rocky Terrains: A Case Study of Dutsin-Ma Town
Bibliographic record
Abstract
The Research is aimed at identifying the spatial distribution and variation of mobile phones network quality in the study area which is dominated by irregular topography, inselbergs and rock outcrops. To achieve this aim, fifty (50) sampled areas were selected in Dutsin-Ma Town based on the nature of their rugged terrain and rocky landscape. Smart phones were used to collect Ultra-High Frequency (UHF) data using systematic grid sampling technique to determine the network strength of MTN, GLOBACOM, AIRTEL, and ETISALAT. A total of one hundred (100) structured questionnaires were also distributed to residents to obtain the necessary information to validate the results from users’ perspective. The research found out that generally Dutsin-Ma is a not a town with good network as average (48%) and poor (36%) network quality dominates the area; only 16% of the areas have good network quality. From the questionnaire survey, majority (43%) of the respondents are using MTN because of its high quality and resistance especially in areas with rock outcrops. It was also found that areas around Motel, Gidan Ruwa, Hayin Gada, all of relatively moderate relief (512-578m) has the best Network; Unguwar Tsamiya, Dan Kauye and Tsohuwar Kasuwa have average Network while areas like Unguwar Alkali and Unguwar Wakaji have poor network quality. It was therefore concluded that network challenges in some areas have (in addition to other factors) a strong connection with elevation, rugged terrain and rocky landscape. However, in some few instances (Kadangaru and Makarantar Gabas), quality of some network like GLO and Airtel increases with altitude. It was also found that rugged terrains constitute a barrier to successful installation of masts in many locations. The research recommended adequate town and landuse planning that will ensure improved spatial distribution of masts for quality phone signals of all networks across Dutsin-Ma area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".