Soybean (Glycine max) cultivar tolerance to saflufenacil
Bibliographic record
Abstract
Miller, R. T., Soltani, N., Robinson, D. E., Kraus, T. E. and Sikkema, P. H. 2012. Soybean (Glycine max) cultivar tolerance to saflufenacil. Can. J. Plant Sci. 92: 1319-1328. Six field studies were conducted over a 2-yr period (2009 and 2010) at three Ontario locations to determine the sensitivity of 12 glyphosate-resistant soybean cultivars to saflufenacil applied preemergence (PRE). The level of crop injury was dependent on environmental conditions shortly after application. When soybean emergence was delayed due to cool, wet conditions following planting, 52 and 59 g a.i. ha-1 of saflufenacil resulted in 10% injury 1 wk after emergence (WAE) in cultivars OAC Hanover and RCAT Matrix, respectively. In the other environments, greater than 200 g a.i. ha-1 of saflufenacil was required to induce the same level of injury at 1 WAE. Injury decreased with time; however, the more sensitive soybean cultivars were unable to recover from early-season injury sustained under adverse environmental conditions. A hydroponic bioassay was developed to screen differences in soybean tolerance to saflufenacil. OAC Hanover was more sensitive than all the other cultivars in both field and hydroponic testing (P<0.05). OAC Hanover yield was reduced regardless of environmental conditions. Under cool, wet conditions, 22 g a.i. ha-1 of saflufenacil resulted in a 10% yield reduction, while 46 g a.i. ha-1 was needed under warm dry conditions. All other cultivars required between 82 and 146 g a.i. ha-1 to obtain the same level of yield reduction. This research demonstrates that there is a difference in soybean cultivar sensitivity to saflufenacil applied PRE.
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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".