Spatial-Temporal Dynamics of Vegetation Cover in a Diversity Hotspot for the Conservation of Brazilian Cerrado
Bibliographic record
Abstract
This work investigates the spatial-temporal dynamics of land use and vegetation covers in a conservation area of Cerrado, in the county of Currais, Piauí, in which the economy depends on large agricultural projects. We used maps of a 32-year time series (1985 to 2017) of land use and cover provided by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas). We assessed six classes of land uses and vegetation covers: forest, savanna, grassland, agriculture/pasture, non-vegetated area, and water bodies. There was a fast increase in pressure on natural ecosystems from 1985 and 2017, primarily from 2000. The land use for agriculture and pasture increased from 0.26% (726.93 ha) in 2000 to 16% (50,772.63 ha) in 2017. During this period, the native vegetation decreased 15.90%, with savannas suffering the largest loss in hectares of vegetation (41,663.73 ha), followed by the forests (9,837.35 ha). The grassland cover, non-vegetated area, and water bodies remained unchanged. These results provide essential information for decision making and can be used to guide public policies for the conservation, monitoring, and sustainable management of remnant vegetation areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".