The Future of Madre de Dios
Bibliographic record
Abstract
Madre de Dios in Peru is one of the most biodiverse regions on Earth, and it is a central piece for connecting protected areas within the Vilcabamba–Amboro Conservation Corridor. As revealed with satellite imagery, the exceptional landscape of Madre de Dios is threatened by unplanned development along the Interoceanic Highway, which bisects the region from north to south. Land-use changes in the last 25 years have reduced landscape connectivity and degraded the ecosystem services on which Madre de Dios’s inhabitants rely. The Smithsonian Center for Conservation and Sustainability developed a new tool to help Madre de Dios’s stakeholders define a common vision for the future of their region: the Smithsonian Working Landscape Simulator. The Future of Madre de Dios presents the framework and implementation of this participatory, holistic, and quantitative tool. The study contemplates four scenarios of future changes for the region: current trends, expansion of alluvial gold mining, land planning, and landscape conservation. The land-cover changes expected under each scenario until 2040 are modelled, and the resulting landscapes are evaluated for 15 indicators of success, covering economic prosperity, human well-being, and environmental integrity. This book illustrates the results from these analyses and presents recommendations that will contribute to the promotion of sustainable development in Madre de Dios.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".