Cellulose factories: advancing bioenergy production from forest trees
Bibliographic record
Abstract
Fast-growing, short-rotation forest trees, such as Populus and Eucalyptus, produce large amounts of cellulose-rich biomass that could be utilized for bioenergy and biopolymer production. Major obstacles need to be overcome before the deployment of these genera as energy crops, including the effective removal of lignin and the subsequent liberation of carbohydrate constituents from wood cell walls. However, significant opportunities exist to both select for and engineer the structure and interaction of cell wall biopolymers, which could afford a means to improve processing and product development. The molecular underpinnings and regulation of cell wall carbohydrate biosynthesis are rapidly being elucidated, and are providing tools to strategically develop and guide the targeted modification required to adapt forest trees for the emerging bioeconomy. Much insight has already been gained from the perturbation of individual genes and pathways, but it is not known to what extent the natural variation in the sequence and expression of these same genes underlies the inherent variation in wood properties of field-grown trees. The integration of data from next-generation genomic technologies applied in natural and experimental populations will enable a systems genetics approach to study cell wall carbohydrate production in trees, and should advance the development of future woody bioenergy and biopolymer crops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".