Retention of Tannin‐C is Associated with Decreased Soluble Nitrogen and Increased Cation Exchange Capacity in a Broad Range of Soils
Bibliographic record
Abstract
Phenolic compounds, called tannins, can be retained by soil and affect soluble N but have been studied in only a few soil types. Surface samples (0–10 cm), collected from the United States and Canada, were treated with water (Control) or solutions containing procyanidin, catechin, ß‐1,2,3,4,6‐pentagalloyl‐ O ‐D‐glucose (PGG), tannic acid, gallic acid, or methyl gallate. Soluble C and N in treatment supernatants and after incubation (16 h, 80°C) were measured to determine retention of treatment C and effects on soluble N and cation exchange capacity (CEC). Retention varied significantly with treatment (T) and soil order (S) and was greatest for PGG > tannic acid > procyanidin > catechin > methyl gallate > gallic acid and in Alfisols, Aridisols and Mollisols compared Ultisols. However, differences among soil orders were observed only for strongly retained compounds (T × S, P < 0.001). Extraction of soluble N was decreased by gallic acid and tannins, especially PGG, but unaffected by methyl gallate or catechin. All treatments decreased soluble N from Aridisols while Entisols were less affected by tannins (T × S, P < 0.01). Soil CEC was significantly increased by tannins but unaffected by other compounds. However, CEC increased more in Aridisols than in Mollisols or Ultisols and treatment effects were small and unvarying in Ultisols (T × S, P < 0.001). Changes to both soluble N and CEC were linearly related with retention of treatment C. Tannins produced effects associated with improved soil quality on a broad range of soils and may have a role in land management.
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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 | 0.000 |
| 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 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".