Projection of spatially explicit land use scenarios for the São Francisco River Basin, Brazil
Bibliographic record
Abstract
Future land use change in the São Francisco River Basin (SFRB) is critical to the future of regional climate and biodiversity, given the large heterogeneity among the four climate types within the basin. These changes in SFRB depend on the link between global and national factors due to its role as one of the world's major exporters of raw materials and national to local institutional, socioeconomic, and biophysical contexts. In this work, LuccME's spatially explicit land change distribution modeling framework is used, aiming to develop three models that balance global (e.g., GDP growth, population growth, per capita agricultural consumption, international trade policies, and climate conditions) and regional/ scene. Local factors (such as land use, agricultural structure, agricultural suitability, protected areas, distance from roads and other infrastructure projects), are consistent with the global structure Shared Socio-Economic Pathways (SSP) and Representative Concentration Pathways (RCP), namely: SSP1/RCP 1.9 (sustainable development scenario), SSP2/RCP 4.5 (moderate scenario) and SSP3/RCP 7.0 (high inequality scenario). Based on detailed biophysical, socioeconomic, and institutional factors for each region of the São Francisco River Basin, spatially explicit land use scenarios to 2050 were created, considering the following categories: agriculture, natural forest, rangeland, agriculture, rangeland, and forest. mosaic plantation. The results show that the performance of the developed model is satisfactory. The average spatial fitting index between observed data and simulated data in 2019 is 89.48%, the average fitting error percentage corresponding to omissions is 2.59%, and the commission error is approximately 2.16%. Regarding the projected scenarios, the results show that three classes, agriculture, pasture, and mosaic of agriculture and pasture will continue in the same direction (increasing), regardless of the scenario considered, differently to the class of natural forest and forest plantation, which will decrease in scenarios of the middle road and strong inequality, and sustainable development, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".