Selenium bioconcentration in Canadian oat (Avena sativa) from soils treated with nanoscale elemental selenium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it