Interference of Condensed Tannin in Lignin Analyses of Dry Bean and Forage Crops
Bibliographic record
Abstract
Legumes with high concentrations of condensed tannin (pinto bean [Phaseolus vulgaris L.], sainfoin [Onobrychis viciifolia Scop.], and big trefoil [Lotus uliginosus Hoff.]), were compared to a selection of forages, with low or zero condensed tannin (smooth bromegrass [ Bromus inermis Leyss], Lotus japonicus [Regel] K. Larsen, and alfalfa [Medicago sativa L.]), using four methods to estimate fiber or lignin. Protocols were validated by using semipurified condensed tannin polymers in adulteration assays that tested low-lignin tissue with polyphenolic-enriched samples. The effect on lignin assay methods by condensed tannin concentration was interpreted using a multivariate analysis. There was an overestimation of fiber or lignin in the presence of condensed tannin in the acid detergent fiber (ADF) and Klason lignin (KL) assays compared to that in the thioglycolic acid (TGA) and acid detergent lignin (ADL) methods. Sulfite reagents (present in TGA lignin method) or sequential acidic digests at high temperatures (ADF followed by ADL) were required to eliminate condensed tannin. The ADF (alone) and KL protocols are not recommended to screen nonwoody plants, such as forages, where condensed tannin has accumulated in the tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".