Sustainable Land Use Prediction in Light of Agroforestry Systems in Response to the Changing Scenario of Land Cover
Bibliographic record
Abstract
Change detection of land-cover to recommend the future directions of land-use is indispensable for sustainable development and the proper utilization of land resources. In this research, unsupervised classification maps produced using images of Landsat 8 OLI from 2013 until 2021 (with a 4-year interval) reveal important land-cover changes, along with their drivers, in Kapasia, Bangladesh. Overall, a substantial increase in paddy (24.7% to 27.2%) and urban (3.5% to 10.1%) and a decrease in homestead (67.5% to 59.3%) and forest (4.2% to 3.4%) were observed within the time interval. To direct the land-use towards long-term biodiversity and sustainability of the region, it is important to implement types of agroforestry systems as the observed decrease in homestead and forest areas are alarming. Agroforestry practices will not only have a positive environmental impact but can help diversify food systems, increase economic return and optimize natural resource use.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".