An Assessment of Mining Activities Impact on Vegetation in Bukuru Jos Plateau State Nigeria Using Normalized Differential Vegetation Index (NDVI)
Bibliographic record
Abstract
The study area has a pathetic and deplorable condition of landuse/ landcover. The vegetal cover in the area has to be removed from the activities of tin mining which consequently resulted into adverse environmental effect such as erosion. Different forms of human induced stress such as tin mining and heavy rainfall have severely degraded soils on the Jos Plateau. Such degradation problems are also caused by deforestation, inappropriate farming system, bush burning and over-grazing which are hostile to the environment. The impact of tin mining has greatly affected the natural ecology of the study area Bukuru. Micro and macro organisms and plants have been stripped off their natural habitat due to tin mining activities. This paper therefore, assesses the mining activities impact on the vegetation in Bukuru area of Jos plateau in Nigeria. Normalized Differential Vegetation Index (NDVI) techniques was adopted to Maps effect of tin mining on the vegetation for the period between 1975 and 2007 using LandSat satellite data. The result of the differential vegetation index analysis reveals a decline in vegetated surfaces in 1986 ranging from 0.04 to 0.58 indicating 0.05 and continuous loss in vegetation over the study area in 2007 (vegetated surface decrease by 0.08 between 1986 and 2007). The decrease in vegetated surface is due to intensive mining and cultivation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".