Miscanthus: a promising feedstock for lignocellulosic ethanol industry in Ontario, Canada
Bibliographic record
Abstract
The life cycle of ethanol derived from miscanthus has been evaluated to determine its environment and economic viability. Net energy consumption, production cost and emission are estimated considering three scenarios (S1: all classes of land; S2: prime land; S3: marginal land, are used for miscanthus cultivation). Depending on the scenarios net energy consumption, production cost and emissions are found to be varied from 12.1 to 12.5 GJ m<sup>-3</sup>, 776.7 to 811.3$ m<sup>-3</sup> and 0.7 to 1.3 t-CO<sub>2</sub>e m<sup>-3</sup>, respectively. Although energy consumption and production cost is slightly varied among the scenarios, the variation seems to be robust in the case of GHG emissions, where carbon dynamics play an important role. This study revealed that miscanthus is a promising feedstock for ethanol even if it is grown on marginal land which may abate competition with food crops and improve the farm economy in Ontario, Canada.
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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 | 0.000 | 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 | 0.000 |
| 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".