Assessing the Impact of Drought-Induced Abiotic Stress on the Content and Composition of Douglas-Fir Lignin
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Drought is one of the most concerning stress factors for lignocellulosic biomass, decreasing productivity and altering its physical and chemical properties and, consequently, its value as a renewable feedstock. Accelerating climate change is expected to result in increasingly frequent and severe droughts, disproportionately impacting northern latitudes. This study investigates the impact of drought-induced abiotic stress on the composition of Douglas-fir ( Pseudotsuga menziesii ) wood, focusing on lignin content and composition. Klason lignin analysis, solid-state 13 C NMR, and Py-GC/MS all determine a ∼4%–5% higher lignin content in the drought-stressed wood compared to the control. The chemometric modeling of Py-GC/MS data provides quantitative molecular indicators of an increased lignin content in wood due to drought stress. Moreover, quantitative 31 P NMR results reveal evidence that drought stress may alter the composition of lignin in Douglas-fir wood. In particular, an increase in phenolic hydroxyl groups and a decrease in aliphatic hydroxyl groups indicate a more hydrophobic lignin, which could promote lignin–enzyme interactions and inhibit the enzymatic hydrolysis of biomass. These findings highlight important indicators of compositional changes in Douglas-fir wood as a biorefinery feedstock under shifting climates. Further investigation will be necessary to better understand lignin’s role in the drought response of Douglas-fir for fundamental insights and practical selective breeding strategies.
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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".