Spatial Analysis of Sustainable and Agricultural Development in Southern Brazil
Bibliographic record
Abstract
Abstract This work sought to create and analyze a Sustainable and Agricultural Development Index (SADI) for the municipalities that make up the Southern Region of the country. For this, the methodology used was the factor analysis through the analysis of principal components and, subsequently, the exploratory analysis of spatial data (ESDA) was performed, verifying the distribution of the indicator. The main results revealed that the values found for the indicators showed a medium-low degree, considering that the highest value found was 0.5885 and the others presented values below 0.5. The municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172). Regarding the ESDA, it showed strong evidence of a positive and significant spatial correlation between the municipalities analyzed, in addition, there was a high disparity in the sustainable and agricultural development of the municipalities that make up these states.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".