The natural and social history of the indigenous lands and protected areas corridor of the Xingu River basin
Bibliographic record
Abstract
The 280,000 km² Xingu indigenous lands and protected areas (ILPAs) corridor, inhabited by 24 indigenous peoples and about 215 riverine (ribeirinho) families, lies across active agriculture frontiers in some of the historically highest-deforestation regions of the Amazon. Much of the Xingu is anthropogenic landscape, densely inhabited and managed by indigenous populations over the past millennium. Indigenous and riverine peoples' historical management and use of these landscapes have enabled their long-term occupation and ultimately their protection. The corridor vividly demonstrates how ILPAs halt deforestation and why they may account for a large part of the 70 per cent reduction in Amazon deforestation below the 1996-2005 average since 2005. However, ongoing and planned dams, road paving, logging and mining, together with increasing demand for agricultural commodities, continued degradation of upper headwaters outside ILPA borders and climate change impacts may render these gains ephemeral. Local peoples will need new, bottom-up, forms of governance to gain recognition for the high social and biological diversity of these territories in development policy and planning, and finance commensurate with the value of their ecosystem services. Indigenous groups' reports of changing fire and rainfall regimes may themselves evidence climate change impacts, a new and serious threat.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".