Fertilization Enhances Grain Inorganic Arsenic Assimilation in Rice
Bibliographic record
Abstract
Abstract To investigate how soil fertilization/amendments alters arsenic speciation grain off-take in paddy rice, rice was grown to maturity in growth chambers fertilized with standard mineral fertilizer, wood ash (rich in silica), pig slurry (rich in organic matter), and non-amended control. The soil was sourced from a Chinese paddy field. The primary fertilized elements (nitrogen, phosphorus, potassium) were kept constant across treatments. Porewater chemistry and soil microbiology were monitored throughout the experiments. Total grain arsenic, sum of inorganic arsenic and dimethylarsinic acid (DMA), was significantly different between treatments (P = 0.024), with inorganic arsenic varying from 0.025 to 0.08 mg/kg and DMA from 0.08 to 0.16 mg/kg for control compared to fertilized, respectively. Fertilizer source made no difference to arsenic speciation concentration in grain. Porewater analysis found that as anaerobism set in, inorganic arsenic, phosphorus and manganese greatly lowered in concentration. Methylated arsenic species concentrations increased over time, concurrent with an increase in pH, decrease in Eh, and increase in total organic carbon and iron, with no strong treatment effects, except for pig slurry that enhanced pH and decreased Eh. Methanogenic archaea, sulfate-reducing bacteria and Acidobacteria increased with time and some Actinobacteria and Firmicutes increased due to slurry, but then decreased with time (P < 0.01). Methanogenic archaea and sulphate-reducing bacteria correlated positively with porewater DMA and negatively with porewater inorganic arsenic (P < 0.05). Genera within the Actinobacteria and Burkholderiaceae correlated negatively with DMA, while genera with iron-reducing capacity (Clostridiales) correlated positively with porewater inorganic arsenic and DMA (P < 0.05).
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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".