Cluster Analysis Referring to Rural Enterprises of Sugarcane Local Productive Arrangement (LPA) in Quirinópolis, Brazil
Bibliographic record
Abstract
In Brazil in recent decades, the the dynamics of land use widened the agricultural frontier for sugarcane cultivation by modifying and replacing intensively traditional and pasture crops. It is proposed to examine the aggregating characteristics of rural enterprises that are part of the sugarcane agribusiness productive arrangement, evaluating their profiles, providing a more effective understanding of their socioeconomic sustainability. The quantitative approach was adopted applying statistical tests for variable selection and multivariate statistical techniques (cluster analysis) to evaluate rural enterprises. The results indicated two clusters with peculiar profiles, and the average distance between farm and agribusiness (± 22 km) and succession capacity (± 2.5 points) of both are similar. The other variables were discrepant (P <0.05), in cluster 1 the very negative rural exodus (-48%) and in cluster 2 positive (23%). Operating costs in relation to compensation for Cluster 1 was 61%, much higher than cluster 2 with 6% on average. It was concluded that through cluster analysis that the contract variables and the size of the establishment are the most significant factors directly influencing the rural exodus and production costs. These observations contribute to the creation of sectorial policies for the use of land and regional economic development, as such imply in a theoretical consolidation of precepts on the sugarcane expansion, as such also imply, under the perspective of the rural practice, in elements for the improvement in planning the agricultural enterprise.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".