What influences forest clearing decisions in shifting cultivation systems? Evidence from Western Amazonia
Bibliographic record
Abstract
Shifting cultivation systems create disturbances in tropical landscapes by converting forests into agricultural land for a temporary period.These disturbances may induce adverse impacts on not only the ecosystem but also human livelihoods by decreasing biodiversity and aggravating climate change.Since preserving biodiverse old-growth forests has a higher conservative priority than fallows, we are interested in identifying the factors that drive farmers to clear old-growth forests over secondary forest fallows in shifting cultivation systems.Using survey data collected previously as part of the PARLAP project, we conducted exploratory and multivariate regression analyses to examine the factors that influence forest clearing decisions on plot location and forest type.Community-level factors (e.g., community age, initial aquatic endowment, and land availability) as well as biophysical factors (e.g., percentage of Holocene soils, old-growth forests availability) were found to predict the probability of clearing old-growth forests better than the household-level factors.This study provides useful insights for policymakers to design more effective policies for preserving old-growth tropical rainforests.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".