Size-resolved distribution of trace elements in lysimeter soil solutions under contrasting long-term agricultural management to assess their bioavailability
Bibliographic record
Abstract
The chemical species of trace elements (TEs) in agricultural soils is highly variable under diverse conditions, requiring tools with clear resolution and minimal disturbance for exploration. A novel surgical (316L) stainless steel (SS) lysimeter with a 5 μm pore size was developed to collect field soil solutions. The size-resolved distribution of TEs were characterized into total (nitric acid digestion), particulate (0.45-5 μm), dissolved (<0.45 μm), colloidal (1 kDa to 0.45 μm), and mainly ionic (<1 kDa) fractions in the lysimeter soil solutions. Total concentrations of TEs (dry weight basis) in acid digested Gray Luvisolic soils were analyzed. Most TEs in lysimeter soil solutions were present in particulate phases, relevant to their geochemical affinities and occurrences in soil minerals. Among dissolved fractions, As, Ba, Co, Li, Mn, Tl, and V existed as mainly ionic species in the soil solutions. Copper, Pb, Al, Th, and U showed variable associations with dissolved organic matter (DOM) and/or inorganic colloids among agricultural treatments. Inorganic NPKS or NKS fertilizer applications with lower pH (5.25-5.74) enhanced mobility and potential bioavailability of Ba, Co, Li, Mn, and Pb present in mainly ionic species, compared with other locations (pH 5.82-6.37). Manure application exhibited a dual effect, potentially increasing bioavailability for As, Tl, and V due to probably enhanced cation exchange capacity (CEC), while also facilitating specific adsorption of Cu and U on DOM, potentially reducing their bioavailability depending on DOM molecular weight. Colloidal and ionic Al and Th concentrations were higher in forest soils than agricultural soils, with extremely low potential bioavailability of Th attributed to strong precipitation with inorganic colloids and adsorption on DOM. The lysimeter sampling and size fractionation method provided a clear insight into agricultural effects on TE distributions and enhancing understanding of agricultural soil health in terms of TE bioavailability in situ.
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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 | 0.001 | 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 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".