Effect of ammonium sulfate and water hardness on glyphosate and glufosinate activity in corn
Bibliographic record
Abstract
Soltani, N., Nurse, R. E., Robinson, R. E. and Sikkema, P. H. 2011. Effect of ammonium sulfate and water hardness on glyphosate and glufosinate activity in corn. Can. J. Plant Sci. 91: 1053-1059. Eight field trials were conducted over a 3-yr period (2008 to 2010) near Harrow and Ridgetown, Ontario, to evaluate the effect of water hardness (distilled: 0 ppm; intermediate: 353 ppm; and very hard 1799 ppm) on full label doses of glyphosate (900 g a.e. ha-1) and glufosinate (400 g a.i. ha-1) [with and without ammonium sulfate (AMS) at 2.5 L ha-1] efficacy in corn. There was no effect of water hardness on control of velvetleaf (ABUTH), redroot pigweed (AMARE), common lambsquarters (CHEAL), and annual grasses green foxtail (SETVI) and barnyardgrass (ECHCG) when glyphosate was applied with or without the AMS. There was also no difference in yield of corn with various water sources when glyphosate was applied with or without AMS. Glyphosate applied with various water sources with or without AMS controlled ABUTH, AMARE, CHEAL, and annual grasses better than glufosinate with or without AMS. Glufosinate with AMS, especially at the 1799 ppm water hardness, generally controlled ABUTH, AMARE, and CHEAL better than glufosinate without AMS, but there was no improvement in annual grass control. Contrasts indicated an 11% increase in yield when glufosinate was applied with AMS compared with when applied without AMS. Based on these results water hardness and AMS had little benefit on the efficacy of glyphosate in corn; however, efficacy of glufosinate was improved when applied with AMS at high water hardness.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".