Land use/land cover forecast and urban sprawl analysis in a Brazilian city in the Atlantic Forest Biome
Bibliographic record
Abstract
Abstract. Urban sprawl, a consequence of urban growth, profoundly impacts land use/land cover, particularly in regions like the Brazilian Atlantic Forest, as this Biome hosts most of the Brazilian population and is recognized as a global biodiversity hotspot. This study uses geospatial techniques, the Cellular Automata model, and structural landscape metrics to simulate future urban land use changes for 2050. The simulation suggests a significant increase in urban areas by 2050, changing from 32.1% in 2020 to nearly 43% by 2050, mainly encroaching upon agricultural lands. Conversely, forest fragments are projected to decline (lost 3% of areas), and the connectivity analysis highlighted the loss along the rivers, emphasizing the need for proactive conservation strategies. Preserving periurban agriculture is vital for food security and sustainable development, while innovative management of riparian ecosystems enhances urban biodiversity and citizen well-being. Sustainable urban planning and conservation efforts are imperative to mitigate the adverse effects of urban sprawl and foster resilient cities. This research provides crucial insights for decision-makers aiming to balance urban development with environmental preservation in the face of rapid urbanization.
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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 | 0.001 |
| 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".