Loss of potentially toxic elements to snowmelt runoff from soils amended with alum, gypsum, and Epsom salt
Bibliographic record
Abstract
Soil amendment effects on the mobility of potentially toxic elements (PTEs) have been hardly investigated under snowmelt flooding conditions. This research quantifies and compares the loadings of arsenic (As), copper (Cu), nickel (Ni), selenium (Se), vanadium (V), and zinc (Zn) to snowmelt from unamended, alum-, gypsum-, and Epsom salt-amended soils from a manured agricultural field and a non-manured agricultural field. In the fall of 2020, amendments were surface applied at a rate of 2.5 Mg ha−1 to field plots with four replicates. Runoff boxes were installed at the plots’ edge to collect winter snow. In the spring of 2021, the snowmelt in each box was pumped out, and volume was recorded until all snow in the boxes had melted. Concentrations of PTE and other cations and pH were measured in a subsample of the snowmelt. The snowmelt from the manured field had higher Ni, Se, and V loads than that from the non-manured field. There were no significant differences in snowmelt PTE loads between the amended soils and the unamended controls at each field. Although not statistically significant, the Epsom salt-amended treatment resulted in a 75% reduction in Se loading and a 44% reduction in V loading, while the gypsum-amended treatment showed a 38% reduction in Ni loading compared to the unamended treatment in the manured soil. Overall, our findings from a single season using both manured and non-manured fields suggest that alum, gypsum, and Epsom salt additions did not significantly alter the mobility of the studied PTEs during the spring snowmelt period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".