Governing Constraints of Chromium(VI) Formation from Chromium(III)-Bearing Minerals in Soils and Sediments
Bibliographic record
Abstract
The potential for geogenic Cr(VI) contamination is vast, yet it is difficult to predict susceptible environments as groundwater Cr(VI) concentrations vary significantly, even within a given aquifer, due to physical and hydrologic heterogeneity. The physical structure of soils and sediments exerts a dominant control on Cr(VI) production by dictating the separation distance of reactive phases, the diffusion distance from Cr(VI) generation sites to advecting groundwater, and by influencing infiltration rates and porewater velocity. Here, we used a dual-pore domain model to investigate the relative control of these parameters on Cr(VI) production. The reaction distance between Cr(III)-bearing minerals and Mn oxides predominantly controls Cr(VI) export to advecting groundwater, while changes in diffusion distance between sites of Cr(VI) generation and advective flow channels generally have little impact on steady-state Cr(VI) concentrations. Changes in Cr(VI) diffusion distance can, however, increase the time required for groundwater Cr(VI) concentrations to reach a steady-state; thus, under fluctuating hydrologic and biogeochemical conditions, long diffusion distances still have the potential to suppress Cr(VI) supply to advecting water. Furthermore, we show that high porewater flow velocities effectively dilute Cr(VI) diffusing from soil/sediment aggregates, thus minimizing Cr(VI) concentrations relative to lower porewater velocities. The strong control that the physical/hydrologic parameters exert on Cr(VI) production appears to overwhelm the impact of Cr(III)-mineral solubility within soils and sediments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".