Barley (<i>Hordeum vulgare</i>) seedling growth declines with increasing exposure to silver nanoparticles in biosolid-amended soils
Bibliographic record
Abstract
Silver nanoparticles (AgNPs), a component of many consumer products, are considered an environmental risk due to the broad-spectrum toxicity of Ag+ to non-target organisms. Most AgNPs released from consumer products will end up as biosolids in wastewater treatment plants, which are often applied as a fertilizer to agriculture. Land application of biosolids may add AgNPs to the soil–plant system, with unknown consequences. This study investigated the growth of Hordeum vulgare seedlings, Ag bioconcentration and distribution in shoot and root tissues of barley exposed to biosolid-amended Delacour and Organization for Economic Co-operation and Development (OECD) soils spiked with AgNPs (up to 366 mg Ag kg−1 dry soil). In both soils, root and shoot growth declined linearly as the concentration of AgNPs increased. Barley had higher Ag bioconcentration values when grown in the OECD soil than in the Delacour soil. Silver bioavailability was greater in the OECD soil due to its physicochemical properties, such as low calcium concentration and acidic pH, relative to the Delacour soil. Barley seedlings exhibited morphological changes, including smaller shoots and shorter, thick roots after 14 d exposure to AgNPs. We conclude that plant structural responses, particularly changes in root biomass, could be an early diagnostic of seedling exposure to AgNPs in biosolid-amended soils.
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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 | 0.001 |
| 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".