The effect of hard water, spray solution storage time, and ammonium sulfate on glyphosate efficacy and yield of glyphosate-resistant corn
Bibliographic record
Abstract
Mahoney, K. J., Nurse, R. E. and Sikkema, P. H. 2014. The effect of hard water, spray solution storage time, and ammonium sulfate on glyphosate efficacy and yield of glyphosate-resistant corn. Can. J. Plant Sci. 94: 1401–1405. Effects of ammonium sulfate (AMS) on reduced glyphosate rates are well documented; however, these rates are not used by farmers. Studies in 2011 and 2012 determined the effects of AMS and hard water on glyphosate applied at a field rate. AMS (0 or 2.5 L ha−1) and glyphosate (900 g a.e. ha−1) were added to distilled or hard water carriers. Glyphosate plus AMS, in either distilled or hard water, did not usually affect velvetleaf, pigweed species, common ragweed, common lambsquarters and large crabgrass above-ground biomass and density 8 wk after treatment (WAT); however, velvetleaf biomass was reduced with the addition of AMS to glyphosate. Compared with the weedy control 8 WAT, glyphosate plus AMS reduced velvetleaf biomass by 93%, while treatments with no AMS reduced biomass by 77%. In distilled water, 4.8 common lambsquarters plants m−2 remained compared with 7.8 plants m−2 with glyphosate in hard water. For corn yield, an AMS×water hardness interaction was detected; however, no differences were observed among the treatment combinations and the addition of AMS to glyphosate did not affect yield. Therefore, advocating adding AMS to spray solutions containing glyphosate, especially when a labeled rate is used, should be called into question.
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 | 0.000 |
| 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 | 0.001 | 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".