Sustainable Efficiency of Sugarcane Mills in the State of Sao Paulo: A Data Envelopment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The pursuit of greater competitiveness and efficiency today goes hand in hand with concerns directly linked to sustainable development. The traditional sugar-energy sector, with a strong influence on the economy since colonial Brazilian periods, not only played a pioneering role in replacing fossil fuel with renewable resources but is also characterized by substantial production-related differences. The Brazil is currently the largest global producer of sugarcane, and the state of São Paulo, in southeastern Brazil, leads this production. The objective of this research was to analyze the sustainable efficiency of sugarcane mills in the state of São Paulo, through Data Envelopment Analysis (DEA). For this, the work was based on the Triple Bottom Line, considering environmental, economic and social approaches to the performance of the mills. Regarding the main results, it was possible to notice that the production scale factor favored large mills in some points of analysis, while at other times, small mills were highlighted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it