Quantitative Prediction of Atrazine Sorption in a Manitoba Soil Using Conventional Chemical Kinetics Instead of Empirical Parameters
Bibliographic record
Abstract
How to predict the persistence and leaching of organic chemicals in soils is a long-standing environmental issue. The injection of soil slurries into an HPLC together with separate solution-phase analysis permits the resolution of total sorption into intraparticle-diffused and labile-sorbed fractions. Sorption site stoichiometry was revealed, and two-step sorption was confirmed. This permitted conventional chemical kinetics including Laidler’s integral rate law for second-order kinetics to be adapted to the natural mixture of irregular sorption sites. Quantitative predictions were successfully tested, and the effects of site saturation on the kinetics were demonstrated. Other authors have independently published evidence related to sorption site stoichiometry, by using scanning tunnelling and fluorescence microscopy for sorption onto idealized crystals. This presents opportunities for research advances in two directions. HPLC and microscopy methods could be used together for sorption mechanisms in both environmental and pure crystal systems.
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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 | 0.001 | 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".