Designed for deconstruction – poplar trees altered in cell wall lignification improve the efficacy of bioethanol production
Bibliographic record
Abstract
• There is a pressing global need to reduce the increasing societal reliance on petroleum and to develop a bio-based economy. At the forefront is the need to establish a sustainable, renewable, alternative energy sector. This includes liquid transportation fuel derived from lignocellulosic plant materials. However, one of the current limiting factors restricting the effective and efficient conversion of lignocellulosic residues is the recalcitrance of the substrate to enzymatic conversion. • In an attempt to assess the impact of cell wall lignin on recalcitrance, we subjected poplar trees engineered with altered lignin content and composition to two potential industrial pretreatment regimes, and evaluated the overall efficacy of the bioconversion to ethanol process. • It was apparent that total lignin content has a greater impact than monomer ratio (syringyl : guaiacyl) on both pretreatments. More importantly, low lignin plants showed as much as a 15% improvement in the efficiency of conversion, with near complete hydrolysis of the cellulosic polymer. • Using genomic tools to breed or select for modifications in key cell wall chemical and/or ultrastructural traits can have a profound effect on bioenergy processing. These techniques may therefore offer means to overcome the current obstacles that underpin the recalcitrance of lignocellulosic substrates to bioconversion.
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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 | 0.001 |
| 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".