Using <sup>13</sup>C nuclear magnetic resonance spectroscopy for the study of northern hardwood tissues
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a useful tool for examining the structural chemistry of natural organic matter. The use of cross-polarization and magic-angle spinning to study 13 C functionality (CPMAS 13 C NMR) is convenient, but not always quantitative. We used various 13 C NMR techniques to examine the structural chemistry of bark and wood of sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.), and yellow birch (Betula alleghaniensis Britt.). Spin counting experiments showed that 87%97% of the 13 C in the samples was observable by CPMAS 13 C NMR. A comparison of CPMAS and Bloch decay experiments revealed few differences in spectral properties. Together, these results suggest that CPMAS 13 C NMR is quantitative for these tissues. We observed little variation in the structural chemistry of wood, either among samples of the same species or among species. Within-species variations in bark chemistry were greater than in wood, probably because of variations in environmental conditions. However, we observed no significant differences in bark chemistry among the species. Bark and wood chemistry differed significantly, with the bark spectra displaying greater contributions from lignin, suberin, waxes, and resins. Hardwood spectra differ from softwood spectra in the aromatic C regions because of the contribution of syringyl units to hardwood lignin. Hardwood bark appears to contain less tannins than softwood bark. Together, the quantitative and qualitative features of CPMAS 13 C NMR spectra are useful for studying the ecology of living and detrital wood and bark.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".