Characterization of High‐Tannin Fractions from Humus by Carbon‐13 Cross‐Polarization and Magic‐Angle Spinning Nuclear Magnetic Resonance
Bibliographic record
Abstract
Condensed tannins can be found in various parts of many plants. Unlike lignin there has been little study of their fate as they enter the soil organic matter pool and their influence on nutrient cycling, especially through their protein-binding properties. We extracted and characterized tannin-rich fractions from humus collected in 1998 from a black spruce [Picea mariana (Mill.) Britton et al.] forest in Canada where a previous study (1995) showed high levels (3.8% by weight) of condensed tannins. A reference tannin purified from black spruce needles was characterized by solution 13C nuclear magnetic resonance (NMR) as a pure procyanidin with mainly cis stereochemistry and an average chain length of four to five units. The colorimetric proanthocyanidin (PA) assay, standardized against the black spruce tannin, showed that both extracted humus fractions had higher tannin contents than the original humus (2.84% and 11.17% vs. 0.08%), and accounted for 32% of humus tannin content. Consistent with the results from the chemical assay, the aqueous fraction showed higher tannin signals in the 13C cross-polarization and magic-angle spinning (CPMAS) NMR spectrum than the emulsified one. As both tannin-rich humus fractions were depleted in N and high in structures derived from lignin and cutin, they did not have properties consistent with recaldtrant tannin-protein complexes proposed as a mechanism for N sequestration in humus. Further studies are needed to establish if tannin-protein structures in humus can be detected or isolated, or if tannins contribute to forest management problems observed in these ecosystems by binding to and slowing down the activity of soil enzymes.
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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 | 0.000 | 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 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".