The Effect of Sugarcane Straw Aging in the Field on Cell Wall Composition
Bibliographic record
Abstract
Cellulosic ethanol is an alternative for increasing the amount of bioethanol production in the world. In Brazil, sugarcane leads the bioethanol production, and to improve its yield, besides bagasse, sugarcane straw is a possible feedstock. However, the process that leads to cell wall disassembly under field conditions is unknown, and understanding how this happens can improve sugarcane biorefinery and soil quality. In the present work, we aimed at studying how sugarcane straw is degraded in the field after 3, 6, 9, and 12 months. Non-structural and structural carbohydrates, lignin content, ash, and cellulose crystallinity were analyzed. The cell wall composition was determined by cell wall fractionation and determination of monosaccharide composition. Non-structural carbohydrates degraded quickly during the first 3 months in the field. Pectins and lignin remained in the plant waste for up to 12 months, while the hemicelluloses and cellulose decreased 7.4 and 12.4%, respectively. Changes in monosaccharide compositions indicated solubilization of arabinoxylan (xylose and arabinose) and β-glucans (β-1,3 1,4 glucan; after 3 months) followed by degradation of cellulose (after 6 months). Despite cellulose reduction, the xylose:glucose ratio increased, suggesting that glucose is consumed faster than xylose. The degradation and solubilization of the cell wall polysaccharides concomitantly increased the level of compounds related to recalcitrance, which led to a reduction in saccharification and an increase in minerals and ash contents. Cellulose crystallinity changed little, with evidence of silica at the latter stages, indicating mineralization of the material. Our data suggest that for better soil mineralization, sugarcane straw must stay in the field for over 1 year. Alternatively, for bioenergy purposes, straw should be used in less than 3 months.
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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 | 0.000 |
| 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) | 0.001 | 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".