Selenium Application Timing: Influence in Wheat Grain and Flour Selenium Accumulation Under Mediterranean Conditions
Bibliographic record
Abstract
Millions of people have an inadequate supply of selenium (Se) and Se-biofortified crops could prevent such deficiency. In order to establish an effective Se biofortification program under Mediterranean conditions on wheat, the objective of the present study was to evaluate the effect of the Se application timing on the Se accumulation in the grain, yield and protein content. In a field experiment, ten g ha-1 of sodium selenate were foliar-applied at four different growth stages: at 1st node detectable (GS-31); at 5th node detectable (GS-35); at boots just swollen (GS-45); and at 1st spikelet visible (GS-51), in two different growing seasons, 2010-2011 and 2011-2012. The application of Se between GS-35 and GS-45 produced the highest Se accumulation in grain, especially in humid years. The milling process caused Se losses of about 15%. In the special conditions of the Mediterranean area, a proper timing of Se application might have major importance in the Se accumulation in the grain, but due to the rainfall before application, rather than to the plant growth stage.
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.
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 | 0.001 |
| 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".