Effect of selenium fertilizer source and rate on grain yield and selenium and cadmium concentration of durum wheat
Bibliographic record
Abstract
Field studies were conducted for 3 yr at two locations in Manitoba, Canada, to evaluate the effect of various sources and rates of Se fertilizer on crop growth and the Se and Cd concentrations of durum wheat. Selenium concentration in durum wheat grown without Se application ranged from 195 to 532 µg kg-1 over the 6 site-years. Selenium seed coating, “Selcote” commercial granular fertilizer, and a foliar spray of sodium selenate solution applied at Feekes stage 5 all increased Se concentration in durum wheat grain at all locations, without negative effects on crop stand, biomass yield or grain yield. Regardless of treatment, Cd concentration in the grain was generally below proposed regulatory limits. Application of Se did not reduce Cd concentration in the grain, regardless of Se source. Selenium fertilization can be used to increase Se concentration of durum wheat, but is not an effective means of reducing the grain Cd concentration. Key words: Selcote, sodium selenate, seed-coating, selenium, cadmium
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".