Evaluation of Soil Surface Charge Using the Back‐Titration Technique
Bibliographic record
Abstract
Variable surface charge ( Q v ) is one of the most important soil properties controlling ion adsorption on the soil solid phase. In this study, the back‐titration technique was used to determine the Q v of soils with a wide range of properties. The procedure defines the Q v as the OH − consumption by surface reactions corrected for dissolution of the solid phase and other solution reactions (e.g., metal hydrolysis). The Q v , varying from 0 to 80 cmol c kg −1 , was dependent on the pH of the soil suspension and the amount of soil organic matter. We used the non‐ideal competitive adsorption (NICA)–Donnan model to simulate the surface charge, assuming a bimodal distribution of H + affinity on the soil solid phase. With the charge data and Microsoft Excel, the NICA‐Donnan model parameters were optimized. The model provided an excellent fit to the experimental data. When the pH was below 8, the surface charge was dominantly distributed to the Type 1 sites; the Type 2 sites started to contribute to the total surface charge at pH > 8. Multiple linear regressions showed that the charge maxima ( Q max ) of the two sites were related to soil cation‐exchange capacity (CEC) and organic C (Org. C); these significant statistical relationships may be used to estimate the surface charge of soils using values of commonly measured soil parameters.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".