Anthropic Processes and Land-Use Change During 33 Years in Roraima, Northern Amazonia
Bibliographic record
Abstract
The Amazon region has experienced a rapid rate of deforestation and land use change as a result of establishment of agricultural settlements, resulting from public policies designed to promote rural development. We analyzed land use patterns and changes in the central region of Roraima, northern Brazil, testing the hypothesis that the anthropic pressure based on the conversion of natural vegetation (forest ecotone zone and open areas of savanna and campinaranas) on agriculture and pasture, has led to the decline of forest resilience, and has not promoted development in lands converted in agricultural colonization projects, a process exacerbated by practices of burning. Satellite images from between 1984 to 2017, with field-collected data and geoprocessing techniques, allowed interpretation and analysis of seven land-use classes. Agriculturally-based human impacts were greatest in forest areas, with forest loss rates being 6.4 times greater than regeneration rates. The 39.3% reduction in natural non-forest vegetation types exceeded that of forest loss (23.8%). Repeated fires resulted in a 627.1% increase in forest fragmentation in areas heavily impacted by fire. Our study revealed that, over 33 years, deforestation and transitions of land to non-conservation uses did not lead to a system with highly productive agricultural practices, but to extensive impoverished, and degraded subsistence. The main reason was the basic unsuitability of the region´s extremely acidic/dystrophic soils on which settlements have been founded, and the predominance of low-tech, family-based, agriculture and the absence of the required technology for attaining better results.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".