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 13C functionality (CPMAS 13C NMR) is convenient, but not always quantitative. We used various 13C 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 13C in the samples was observable by CPMAS 13C NMR. A comparison of CPMAS and Bloch decay experiments revealed few differences in spectral properties. Together, these results suggest that CPMAS 13C 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 13C NMR spectra are useful for studying the ecology of living and detrital wood and bark.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".