Pathways to positive scenarios for the Amazon forest in Pará state, Brazil
Bibliographic record
Abstract
Abstract: Infrastructure projects and agriculture expansion are increasingly threatening forest conservation in Pará state (Brazil). It becomes necessary to address the implications of these activities on the Amazon complex socio-ecological system, considering both material and non-material aspects of Nature´s Contributions to People (NCP). Multiple studies developed future scenarios for the Amazon, but only a few have focused on discussing positive futures derived from policies and interventions based on conservation and human well-being. Here, we aim at understanding the drivers of forest cover change to produce positive scenarios for the future of the Amazon forest in Pará state. By using the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) conceptual framework, we identified as direct drivers of forest cover change: (i) roads construction; (ii) forest degradation; (iii) hydropower projects; (iv) urban expansion; (v) agriculture and pasture expansion; (vi) rural land occupation; (vii) mining; (viii) climate change. As indirect drivers we identified: (i) energy demand; (ii) population growth; (iii) land prices; (iv) commodity demand; (v) consumption behavior. The development of conservation strategies in the borders of deforested areas is needed given the high demand for Nature´s Contributions to People supply. We also propose policies to address the main drivers of forest cover change, influencing land management and consumption behavior in the state. At last, we envision future positive scenarios that would emerge from policy applications and sustainable actions. Based on our study, we discuss the importance of social learning for developing pathways leading to positive futures that consider the integrity and development of both ecological and social systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".