Physical Chemistry of Irregular Mixtures: A Chemical Kinetics Scan over the Collection of Montmorillonite Sorption Sites
Bibliographic record
Abstract
The simultaneous protection of food crops, soil, and water has been an ongoing issue for over 70 years. The technology required for the control of this has been stalled by one key fact. Soil is the ultimate example of physically and chemically irregular mixtures. Soils are also dynamic. Even soil components are irregular mixtures. The voluminous accumulation of related publications is all based on the assumption that the application of the correct physical chemistry is impossible for such mixtures. The research objective reported here is the demonstration that the correct chemical kinetics can be applied to an irregular mixture. The irregular mixture of montmorillonite sorption sites for atrazine is an example. Montmorillonite is one of the many crystalline aluminosilicates found in the Earth's crust. Weathering will add crystal lattice defects in addition to those predicted by the second law of thermodynamics. An irregular mixture of sorption sites for an organic compound was expected. An irregular mixture can be quantitatively characterized by an experimental scan of its components. An atrazine solution was used for two types of scans. A law of mass action scan over the reaction time revealed one set of sorption sites in which sorption equilibrium was established. This was not found for other sorption sites. Second-order chemical kinetics for sorption yielded the kinetic rate coefficients that characterize nonlinear kinetics. The scans of the kinetic rate coefficients over the numbers of filled sorption sites resolved the mixture of sorption sites into separate sets. These scan methods should be applied to environmentally important cases of contaminated agricultural soils.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".