Partitioning of silver and chemical speciation of free Ag in soils amended with nanoparticles
Bibliographic record
Abstract
BACKGROUND: Knowledge about silver nanoparticles in soils is limited even if soils are a critical pathway for their environmental fate. In this paper, speciation results have been acquired using a silver ion selective electrode in three different soils. RESULTS: Soil organic matter and pH were the most important soil properties controlling the occurrence of silver ions in soils. In acidic soils, more free silver ions are available while in the presence of organic matter, ions were tightly bound in complexes. The evolution of the chemical speciation of the silver nanoparticles in soils was followed over six months. CONCLUSION: During the first few hours, there appeared to be a strong sorption of the silver with soil ligands, whereas over time, silver ions were released, the final concentration being approximately 10 times higher than at the beginning. Ag release was associated with either the oxidation of the nanoparticles or a dissociation of adsorbed silver from the soil surfaces.
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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 | 0.000 |
| 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".