Selenium Concentration in Spring Wheat and Leaching Water as Influenced by Application Times of Selenium and Nitrogen
Bibliographic record
Abstract
Selenium (Se) deficiency in Scandinavian soils is a common problem, and crops generally contain inadequate amounts to meet human need. This study shows a relationship of the Se concentration in spring wheat (Triticum aestivum L., c.v. ‘Helena’) and leaching water with timing of nitrogen (N) [as ammonium nitrate (NH4NO3)] and Se [as sodium selenate (Na2SeO4)] application. Ammonium-nitrate was applied by two methods (i) whole amount at sowing and (ii) in split application as 75% at sowing and 25% at stem elongation. Selenate was applied at cereal growth stages after sowing, e.g., tillering, stem elongation, head emergence, and milking. Split N application in comparison to one N application increased the grain protein content from 12.1 to 13.7 mg g− 1, and grain Se was increased from 0.8 to 1.1 mg kg− 1 when Se was applied at stem elongation and from 0.6 to 0.9 mg kg− 1 when applied at heading. The highest Se concentration in plant was achieved with the split N application and Se application at stem elongation or heading. Selenium leaching losses increased with increasing selenium concentration in the wheat grains. No differences in Se leaching losses were obtained with split N application. Applying selenate and ammonium-nitrate together after tillering increased the grain Se concentration, but did not affect the potential leaching of Se, and thus could be considered as an appropriate time of application of these elements.
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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) | 0.000 | 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 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".