Fourier Transform Infrared Studies of Heterogeneity, Photodegradation, and Lignin/Hemicellulose Ratios within Hardwoods and Softwoods
Bibliographic record
Abstract
There is little information available on the variation in lignin content of growth rings in hardwoods. This study examines whether infrared microscopy can detect intra-incremental differences in the chemical composition of three hardwoods (R. pseudoacacia, P. Americana, and G. triacanthos) and the effect of such differences on the delignification of the hardwoods during weathering. Earlywood has higher lignin content than latewood in R. pseudoacacia and P. americana, but the opposite was found for G. triacanthos. The delignification of the earlywood and latewood during weathering varied for the three species. It was greater in the earlywood of R. pseuoacacia, whereas in P. americana and G. triacanthus it was more pronounced in latewood. Differences in density and lignin content of earlywood and latewood help explain these differences. In addition, a deconvoluting software package was used to determine whether it is possible to estimate the lignin/hemicellulose ratio in softwoods and hardwoods. Results from the 1760-1580 cm(-1) region provided data that can be used to estimate the lignin/hemicellulose ratio of softwoods and hardwoods. This information can be obtained far more easily using infrared microscopy than with conventional wet chemical techniques, potentially allowing characterization of greater numbers of species than has hitherto been possible.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".