High Affinity Sorption Domains in Soil Are Blocked by Polar Soil Organic Matter Components
Bibliographic record
Abstract
Reported correlations between organic contaminant sorption affinity and soil organic matter (OM) structure vary widely, suggesting the importance of OM physical conformation and accessibility. Batch equilibration experiments were used to examine the sorption affinity of bisphenol A, atrazine, and diuron to five soils of varying OM composition. (13)C cross-polarization magic angle spinning NMR spectroscopy was used to characterize the organic carbon chemistry of the soil samples. High sorption by a soil low in O-alkyl components suggested that these structures may block high affinity sorption sites in soil OM. As such, soil samples were subjected to acid hydrolysis, and NMR results showed a decrease in the O-alkyl carbon signal intensity for all soils. Subsequent sorption experiments revealed that organic carbon-normalized distribution coefficient (K(OC)) values increased for all three contaminants. Before hydrolysis, K(OC) values correlated positively with soil aromatic carbon content and negatively with polar soil O-alkyl carbon content. While these correlations were weaker after hydrolysis, the correlation between K(OC) values and soil alkyl carbon content improved. This study suggests that hydrolyzable O-alkyl soil OM components may block high affinity sorption sites and further highlights the importance of OM physical conformation and accessibility with respect to sorption processes.
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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 | 0.000 | 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".