Effect of ammonium sulfate and water hardness on glyphosate and glufosinate activity in corn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it