Land-use change in the Neotropics : regional-scale predictors of deforestation and local effects on carbon storage and tree-species diversity
Bibliographic record
Abstract
Land-use change, and in particular tropical deforestation, is the leading cause of species extinctions globally, and is the second most important source of CO2 emissions after fossil fuel combustion. I examine two policy-relevant questions that relate to tropical deforestation and land use change: (1) At regional scales, what biophysical and infrastructure-related factors are associated with deforestation? and (2) At a local scale, what are some of the impacts of land use change on above- and below-ground carbon stocks and on tree-species richness? The first question was examined for the Brazilian Amazon through spatially-explicit correlation analyses of deforestation and a series of predictor variables that included highways and roads, annual rainfall, dry season length, soil characteristics, site accessibility, and population density. The proximity of a site to roads and highways was the strongest predictor of deforestation, with more accessible sites more likely to be deforested. Dry season length was also a strong, positive predictor of deforestation. The results suggest that current plans to expand road infrastructure in Amazonia will have a significant impact on the forests of the areas transected.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".