Land Use and Land Cover Changes at Hova Farm in Bindura District, Zimbabwe
Bibliographic record
Abstract
Land use and land cover (LULC) change analyses are critical for the sustainable planning and management of natural resources in the face of rapid population growth across the globe. It is believed that LULC changes cause severe environmental challenges such as climate change, biodiversity loss, pollution, alteration to the physical and chemical properties of the soil as well as the destruction of the ozone layer. The main objective of the study was to assess the LULC changes at Hova Farm from 1992 to 2011 using geospatial technologies. Three Landsat images for 1992, 2001, and EMT+ for 2011 were used. The Landsat images had a resolution of 30m by 30m. Five LULC classes of woodland, wooded grassland, cultivated land, bushland and water body were created using the supervised classification maximum likelihood in ENVI 5.0. Field observation and measurements were also used to validate remotely sensed data. The accuracy assessment for the classified maps for 1992, 2001 and 2011 was 88.74%, 86, 67% and 87% respectively. The results indicated that the greatest LULC changes occurred between 1992 and 2001 and was attributed to the fast-track land reform programme and illegal mining activities on the farm. The study recommends the creation of a LULC database for the periodic monitoring and sustainable management of natural resources at both local and national levels in Zimbabwean.
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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 | 0.001 | 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) | 0.001 | 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".