Large-Scale Ethanol Fermentation Through Pipeline Delivery of Biomass
Bibliographic record
Abstract
Issues of traffic congestion and community acceptance limit the size of biomass-processing plants based on truck delivery to about 2 million (M) dry t/yr or less. In this study, the cost of ethanol from an ethanol fermentation plant processing 2 M dry t/yr of corn stover supplied by truck is compared with that of larger plants in the range of 4–38 M dry t/yr supplied by a combination of trucks plus pipelines. For corn stover, a biomass source with a low yield per gross hectare, the cost of ethanol from larger plants is always higher. For wood chips from the boreal forest, a biomass source with a relatively high yield per gross hectare, a plant processing 14–38 M dry t/yr produces ethanol at a 13% reduction in cost compared with a plant producing 2 M dry t/yr supplied by truck. Processing of value-added products, such as chemicals from lignin, would be enabled by larger-scale plants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".