Use of Environmental and Thermodynamic Indicators to Assess the Performance of an Integrated Process for Ethanol Production
Bibliographic record
Abstract
Corn is one of the possibilities for diversification of Brazilian ethanol production. Four scenarios of analysis were established. The environmental dimension was evaluated by the Life Cycle Assessment (LCA) approach, whereas the Thermodynamic performance was verified by applying the techniques of Cumulative Energy Demand (CED) and Cumulative Exergy Demand (CExD). The production of ethanol from corn using wood chips for energy supply of the plant resulted in a homogeneous environmental performance. Factors such direct seeding -and the LHV of the wood for energy support this result. For both Thermodynamic analysis the production of sugarcane ethanol had better indexes because the use of bagasse replaced other sources of primary energy. This result remained for a combined analysis between the two dimensions, which related environmental effects in terms of Climate Change with the aggregation of primary energy consumption for ideal systems.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".