The effect of applied micronutrients (Cu, Zn, Mn, and B) and chloride on annual canarygrass
Bibliographic record
Abstract
Annual canarygrass is highly responsive to chloride (Cl−) fertilizer for increasing grain yield. This responsiveness to Cl− raises the question of whether annual canarygrass is similarly responsive to other micronutrients. The effect of micronutrients, Cu, Zn, B, and Mn with the basal rate of N, P, K, S, and Cl−, on annual canarygrass was studied using two application methods, soil (sideband at seeding) and foliar (3–6 leaf and flag-leaf emergence). Over 4 years, field research was conducted at two Saskatchewan locations, Indian Head and Melfort using a randomize complete block design (RCBD). A foliar application after flagleaf emergence of either Cu, Zn, or Mn provided the best method to increase the concentration of that micronutrient in the leaf tissue during seed filling. Boron concentrations in the leaf were increased during seed filling by soil or foliar treatments containing B at different site-years. Zinc, Cu, and B application had no impact on grain yield. A foliar application of Mn affected grain yield at one of six site-years and requires further investigation. As expected, soil application of macronutrients + Cl− increased grain yield between 29% and 187% compared to unfertilized treatment in five of six site-years. Melfort in 2015 and 2016 had low levels of tissue Cl− in control treatment. The largest grain yield increases occurred at these sites, suggesting the potential of early season tissue testing in identifying Cl− responsive fields of annual canarygrass. Annual canarygrass is not responsive to Cu, Zn, and B but may be responsive to Mn.
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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 | 0.001 | 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".