Sorption of a Xenobiotic Contaminant in Clean and Petroleum-Contaminated Soil: Roles of Water and Xenobiotic Size
Bibliographic record
Abstract
Environmental Context. Soil uptake of xenobiotics (e.g. pesticides) can be a complex phenomenon where it is useful to distinguish readily reversible sorption from longer-term retention. A scheme for doing this using fluorescence detection is presented here, along with application to uptake of a model compound in clean and oil-contaminated soils. Both the wetting of the soil and the size of the xenobiotic seem to be important. The present data concern uptake. Desorption is expected to exhibit dependencies on similar factors. The data have implications for understanding persistence. Abstract. Description of sorption of xenobiotics (e.g. pesticides) into soils requires identification of at least two kinetic components. In the present work, the distinction between ‘labile’ (readily reversible) and ‘non-labile’ (not reversible) uptake was extended, introducing a fluorescence-based method using 9-anthracenepropionic acid as a probe molecule. Study of clean, oil-contaminated wettable, and water-repellent oil-contaminated soils has given new perspectives into the role that water plays in xenobiotic uptake. Non-labile uptake is unimportant in the water-repellent soils; however, non-labile components are observed in both clean and wettable oil-contaminated samples, supporting earlier suggestions that water plays a role in non-labile uptake processes. A soil pre-exposed to water exhibited different labile sorption behavior from one where xenobiotic was added simultaneously with water to an air-dried soil. The comparatively rapid non-labile component of uptake (3 days) of 9-anthracenepropanoic acid by a clean soil contrasted with much longer times in earlier studies of 2,4-D and atrazine. This pointed to another factor influencing the sorption phenomenon. Literature data supports a suggestion that the non-labile component of xenobiotic sorption may be more strongly influenced by the size of the xenobiotic than by the structure (e.g. polarity) of the xenobiotic or soil composition.
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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 | 0.000 |
| 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 | 0.001 |
| 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".