An Assessment of the Growth of Ile-Ife, Osun State Nigeria, Using Multi-Temporal Imageries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examined the use of GIS and Remote Sensing in monitoring the growth and development pattern of Ile-Ife, Osun State, Nigeria over a period of 21 years with a view to predicting its direction of growth. In effect, the study sought to identify and explain the rate and extent of changes in the study area between 1986 and 2007; measure the rate of urban growth in the study area between 1986 and 2007; assess the impact of urban growth on land use patterns; and predict the trend of urban growth in the study area. Data for the study were generated from both primary and secondary sources. Remote Sensing Imagery of Landsat TM 1986, Landsat ETM 2002 and ALOS 2007 were used to measure the extent of growth and to show the effects of this growth on other Land use/Land cover types. Multi-temporal approach was adopted for the study to detect the changes in the imageries. Pixel analysis was employed to identify and compare the type, nature, trend and magnitude of change that occurred in the study area within the slated dates. The observed land use/land cover and population were projected to the next 15 years. The results showed the growth of Ile-Ife and its effects on other land use classes. Pixel analysis revealed that changes occurred in the magnitude and rate of urbanization in the study area between 1986 and 2007. The results were discussed mainly focusing on the trend of urban growth expansion and its effect to the Environment natural resources, farmland and food security and its contribution to climate change. Its implications to urban planning were also discussed and the recommendations made.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it