Risk Economic Viability Focusing on Energy Efficiency in Three Genotypes of Elephant Grass in the Municipality of Alegre, Brazil
Bibliographic record
Abstract
The use, on a large scale, of fossil fuels and their derivatives has devastating long-term consequences for mankind. Therefore is an urgent need to seek new alternatives for sustainable energy production. This fact is one of the great challenges to be faced by researchers worldwide. Within this context, the elephant grass has been standing out successfully in the production of biomass for energy purposes. The purpose in this study was to analyze the economic viability of biomass production of three genotypes of elephant grass for energy purposes and to identify the risk by means of the Monte Carlo simulation. The economic indicators were obtained by calculating the Net Present Value (NPV), the Internal Rate of Return (IRR), and the Profitability Index (PI). To determine the degree of uncertainty, analysis of sensitivity was applied. Results indicated viability for all genotypes, especially the Guaçú/I.Z.2, with IRR of 17.79%. Variation in sale price of grass generates a greater impact on profitability, followed by the labor and fertilization costs. The risk of failure was relatively low, with the exception of Capim Cana D’África, 38.16%. Among the three genotypes studied, the G1 genotype (Guaçú/I.Z.2) stood out as the one with the best economic viability.
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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 | 0.000 | 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".