Selenium bioconcentration in Canadian oat (Avena sativa) from soils treated with nanoscale elemental selenium
Bibliographic record
Abstract
Development of selenium (Se)-enriched agricultural products can increase human daily dietary Se intake in Se-deficient areas. Canadian oat (Avena sativa L. cv. Saddle) is one of the common cereal grains in the world. Previous studies have shown that Se accumulation in oat can be significantly affected by soil Se, but few have dealt with different chemical forms of Se, including emerging nanoscale elemental Se particles (SeNPs). Because SeNPs have unique chemical and physical properties in comparing with bulk elemental Se, this laboratory study determined the effects of soil SeNP treatments of 0, 1, 5, and 10 mg/kg on Se bioconcentration in oat grain, compared with bulk elemental Se or selenate (Na2SeO4). The results showed that the soil SeNP treatments significantly increased Se concentrations in oat grain with an increase in the treatment level from 1 to 10 mg/kg (P < 0.05). The distribution of Se accumulated in oat tissues followed a descending order of root and grain > husk > stem and leaf. While the grain yield was reduced with the higher soil selenate treatments of 5–10 mg/kg, the soil SeNP treatment of 1–10 mg/kg significantly enhanced the oat grain yield, compared with the control. Concentrations of Se in oat grains in the soil SeNP treatments were approximately 7–20-fold higher than were the concentrations of those in the soil bulk elemental Se treatments, but were about 7–26% of the concentrations in oat grains in the soil selenate treatments. This study demonstrated that nanoscale elemental Se particles could be used for development of soil Se-amended fertilisers for Se-biofortified oat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".