Revealing land control dynamics in emerging agricultural frontiers
Bibliographic record
Abstract
The expansion of commodity agriculture into tropical and subtropical woodlands degrades ecosystem functionality, biodiversity, and the livelihood base of millions of people. Understanding where and how agricultural frontiers emerge is thus important. Yet, existing monitoring approaches typically focus on mapping deforestation and do not capture the shifts in land access and ownership that lay the ground for agricultural expansion, thereby missing early stages of frontier development. We develop an approach that captures these early dynamics and apply it to the entire 1,1 million km 2 of the Chaco, a global deforestation hotspot. Through the detection of linear features indicative of land claims and the analysis of their spatial–temporal dynamics, we reveal that the footprint of agricultural frontiers in the region extends far beyond that of deforestation. Most of the Chaco shows signs of land claiming, and although claiming activity is especially concentrated close to active deforestation, emergent claiming in remote parts of the Bolivian and Paraguayan Chaco indicates rapidly growing interest in land in these regions. Finally, the strong spatial correlation between land claiming and the disappearance of smallholder homesteads points to the social repercussions of early agricultural frontier expansion in the Chaco. By offering a transferable template to map land-control indicators at scale, our approach enables a better understanding of frontier processes and more accurate targeting of policy interventions in emerging agricultural frontiers globally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".