Mineralogy and chemistry of some variable charge subsoils
Bibliographic record
Abstract
Colloidal mineralogy is one of the main characteristics of the steady state reached in developed soils. Surface charge and other chemical and physical properties of the soil depend on colloidal mineralogy. It is, therefore, very important to further investigate the clay mineralogy of acidic variable charge subsoils in order to understand better their unusual chemical behavior. The objective of this investigation was to characterize the inorganic colloid mineralogy, chemical (subsoil solution pH and electrical conductivity), and charge properties (PZNC and PZSE) in some variable charge subsoils. Subsoil materials were collected from the southeastern United States and other tropical and subtropical areas around the world. The clay fraction mineralogy in the majority of the subsoils was dominated by the quartet kaolinite, gibbsite, goethite, and hematite. They manifested, however, a significant diversity in their charge and other chemical characteristics because the proportions and contents of mineralogical constituents, particle size distributions, and specific surface areas were very different. The pHKCl values ranged from 3.69 to 5.91. Under such conditions, pure kaolinite and aluminum/iron (Al/Fe) oxides have opposite net surface charges, and acidic subsoils are mixed charge colloidal systems. They have extremely low EC values, varying from 9.9 to 132 μS cm‐1, with corresponding ionic strengths between 0.14 and 1.86 mmol L‐1. They develop towards a “no or little charge state”; and the native pH is near the PZNC or PZSE. The overall charge characteristics and adsorption properties in these heterogenous colloidal systems are clearly a direct function of the relative contents, interactions, and surface reactivity of mineralogical soil constituents in the subsoils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".