Visualising the equilibrium distribution and mobility of organic contaminants in soil using the chemical partitioning space
Bibliographic record
Abstract
Assessing the behaviour of organic chemicals in soil is a complex task as it is governed by the physical chemical properties of the chemicals, the characteristics of the soil as well as the ambient conditions of the environment. The chemical partitioning space, defined by the air-water partition coefficient (K(AW)) and the soil organic carbon-water partition coefficient (K(OC)), was employed to visualize the equilibrium distribution of organic contaminants between the air-filled pores, the pore water and the solid phases of the bulk soil and the relative importance of the three transport processes removing contaminants from soil (evaporation, leaching and particle erosion). The partitioning properties of twenty neutral organic chemicals (i.e. herbicides, pharmaceuticals, polychlorinated biphenyls and volatile chemicals) were estimated using poly-parameter linear free energy relationships and superimposed onto these maps. This allows instantaneous estimation of the equilibrium phase distribution and mobility of neutral organic chemicals in soil. Although there is a link between the major phase and the dominant transport process, such that chemicals found in air-filled pore space are subject to evaporation, those in water-filled pore space undergo leaching and those in the sorbed phase are associated with particle erosion, the partitioning coefficient thresholds for distribution and mobility can often deviate by many orders of magnitude. In particular, even a small fraction of chemical in pore water or pore air allows for evaporation and leaching to dominate over solid phase transport. Multiple maps that represent soils that differ in the amount and type of soil organic matter, water saturation, temperature, depth of surface soil horizon, and mineral matters were evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".