Biologically effective rate of saflufenacil/dimethenamid-p in soybean (Glycine max)
Bibliographic record
Abstract
Miller, R. T., Soltani, N., Robinson, D. E., Kraus, T. E. and Sikkema, P. H. 2012. Biologically effective rate of saflufenacil/dimethenamid- in soybean (Glycine max). Can. J. Plant Sci. 92: 517–531. A total of five field studies were conducted over a 2-yr period (2009, 2010) at three Ontario locations to determine the biologically effective rate of saflufenacil/dimethenamid-p applied preemergence (PRE) for the control of annual weeds in soybean. The rate of saflufenacil/dimethenamid-p required for the control of annual weeds was influenced by environmental conditions. With adequate moisture and above-average temperatures in 2010, between 224 and 374 g a.i. ha-1 of saflufenacil/dimethenamid-p was required for 80% control of common ragweed, common lamb's quarters, and green foxtail 4 wk after treatment (WAT). In contrast, below-average temperatures and excessive moisture in 2009 resulted in higher rates of saflufenacil/dimethenamid-p being necessary for the same level of weed control. Pigweed species were least affected by environmental conditions after application at 4 WAT with only 245 g a.i. ha-1 required for 80% control in both years. By 11 WAT, 320 g a.i. ha-1 or less of saflufenacil/dimethenamid-p was required to achieve 80% control of these species in 2010, while 845 g a.i. ha-1 or more was needed in 2009 for equivalent control. The potential of saflufenacil/dimethenamid-p as a set-up treatment prior to a postemergence (POST) glyphosate application was also examined. Excellent full season control of all weed species was achieved with saflufenacil/dimethenamid-p applied PRE followed by glyphosate POST. However, there was no difference in yield when saflufenacil/dimethenamid-p was followed by glyphosate POST compared with a single glyphosate POST application.
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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 | 0.000 | 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".