Susceptibility of pretreated wood sections of Norway spruce (<i>Picea abies</i>) clones to enzymatic hydrolysis
Bibliographic record
Abstract
The structure of softwoods, which confers resistance to degradation through hydrolysis and decay, currently limits their use for the production of biofuels. However, since wood is very heterogeneous, it is possible that differences in wood properties within and between trees could differentially affect its processability. In this research, heartwood (inner) and sapwood (outer) from Norway spruce ( Picea abies (L.) Karst.) clones were enzymatically hydrolyzed by Trichoderma viride cellulases after concentrated acid pretreatment. Wood sections with two particle sizes were compared based on their susceptibility to enzymatic hydrolysis, evaluated by assaying the formation of hydrolysis products and measured as reducing sugar yield (RSY). We also studied the relationship between RSY and the susceptibility to Heterobasidion parviporum wood decay and whether these traits are reflected in wood density and yield. Wood from the outer section produced more RSY with higher glucan but lower lignin content than wood from the inner section. Furthermore, susceptibility to enzymatic hydrolysis was positively correlated with H. parviporum wood decay, while both processes were negatively correlated with wood density. Our results revealed the importance of clonal trials for identifying suitable lignocellulosic biomass when considering wood properties and indicate that potential genotypes for the production of biofuels are not necessarily the most productive.
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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) | 0.000 | 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 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".