Effects of organic matter on the rate of potassium adsorption by soils
Bibliographic record
Abstract
Soil organic constituents may strongly affect the kinetics of soil chemical processes, including K exchange reactions. We investigated the influence of organic matter on the rate of K adsorption by selected soils (Ferric Ultisol, Orthic Ultisol and Vertisol) using a H2O2 treatment and a K ion-selective electrode technique. In the reaction period of 0–30 s, in which the adsorption was too fast for one to determine rate coefficients of K adsorption, the amount of K adsorbed by the untreated soils was 158–363 mg kg–1, compared with 0.5–47 mg kg–1 for the treated soils. In the reaction period of 30–120 s, K adsorption data based on the first-order kinetics show that rate coefficients of K adsorption by the untreated soils were 47 × 10–5 s–1 (Ferric Ultisol), 59 × 10–5 s–1 (Orthic Ultisol) and 61 × 10–5 s–1 (Vertisol); by contrast, after H2O2 treatment, the rate coefficients were 23 × 10–5 s–1 (Ferric Ultisol), 17 × 10–5 s–1 (Orthic Ultisol) and 42 × 10–5 s–1 (Vertisol). Similar treatment effects were observed for the reaction period of 120–600 s, though the difference in the rate coefficients between the treatments was not as great as that for the reaction period of 30–120 s. These results indicate that organic matter considerably promotes the initial fast rate of K adsorption and has more easily accessible adsorption sites for K compared with mineral constituents of the soils. Key words: Organic matter, kinetics, potassium adsorption, adsorption site, accessibility
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".