An actor-centered, scalable land system typology for addressing biodiversity loss in the world’s tropical dry woodlands
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Bibliographic record
Abstract
Land use is a key driver of the ongoing biodiversity crisis and therefore also a major opportunity for its mitigation. However, appropriately considering the diversity of land-use actors and activities in conservation assessments and planning is challenging. As a result, top-down conservation policy and planning are often criticized for a lack of contextual nuance widely acknowledged to be required for effective and just conservation action. To address these challenges, we have developed a conceptually consistent, scalable land system typology and demonstrated its usefulness for the world's tropical dry woodlands. Our typology identifies key land-use actors and activities that represent typical threats to biodiversity and opportunities for conservation action. We identified land systems in a hierarchical way, with a global level allowing for broad-scale planning and comparative work. Nested within it, a regionalized level provides social-ecological specificity and context. We showcase this regionalization for five hotspots of land-use change and biodiversity loss in dry woodlands in Argentina, Bolivia, Mozambique, India, and Cambodia. Unlike other approaches to present land use, our typology accounts for the complexity of overlapping land uses. This allows, for example, assessment of how conservation measures conflict with other land uses, understanding of the social-ecological co-benefits and trade-offs of area-based conservation, mapping of threats, or targeting area-based and actor-based conservation measures. Moreover, our framework enables cross-regional learning by revealing both commonalities and social-ecological differences, as we demonstrate here for the world's tropical dry woodlands. By bridging the gap between global, top-down, and regional, bottom-up initiatives, our framework enables more contextually appropriate sustainability planning across scales and more targeted and social-ecologically nuanced interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it