Short Communication: Influence of manganese on efficacy of glyphosate in glyphosate-resistant soybean
Bibliographic record
Abstract
Soltani, N., Shropshire, C. and Sikkema, P. H. 2011. Short Communication: Influence of manganese on efficacy of glyphosate in glyphosate-resistant soybean. Can. J. Plant Sci. 91: 1061-1064. Four field trials were conducted from 2007 to 2010 in Ontario to evaluate the effect of various manganese (Mn) formulations (Mn1, Ecoman 5% Mn; Mn2, MangaMax 5.5% Mn; Mn3, ManMax 5.5% Mn; Mn4, Superman 5% Mn; Mn5, Stoller This 5% Mn; Mn6, Nortrace 6% Mn-EDTA (ethylenediaminetetraacetate); Mn7, Nortrace 22% Mn and Mn8, WolfTrax 33% Mn) applied at 2.0 kg actual Mn ha-1 on glyphosate efficacy at 900 g a.e. ha-1 in glyphosate-resistant soybean. The tank mix of glyphosate plus Mn4, Mn6 or Mn8 caused as much as 6, 17 and 4% injury in soybean, respectively. There was minimal crop injury (0-1.4%) with other Mn tank mixes. The addition of Mn4 or Mn6 to glyphosate did not antagonize glyphosate efficacy on the weeds evaluated (AMARE, AMBEL, CHEAL and SETVI). The other Mn formulations antagonized glyphosate efficacy for the control of AMARE, AMBEL, CHEAL or SETVI under some environments. The addition of Mn3 or Mn6 to glyphosate reduced soybean yield as much as 15 and 10% compared with glyphosate alone, respectively. Based on these results, it is recommended that glyphosate and manganese applications be applied sequentially to avoid weed control antagonism and maximize soybean yield.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".