Desiccation in dry edible beans with various herbicides
Bibliographic record
Abstract
Soltani, N., Blackshaw, R. E., Gulden, R.H., Gillard, C. L., Shropshire, C. and Sikkema, P. H. 2013. Desiccation in dry edible beans with various herbicides. Can. J. Plant Sci. 93: 871-877. There is little information available on the effect of diquat, carfentrazone-ethyl, glufosinate ammonium, flumioxazin and saflufenacil applied alone or in tankmix combination with glyphosate as harvest aids in dry bean production under environmental conditions of the various production regions in Canada. A total of 11 field trials were conducted over a 3-yr period (2010, 2011, 2012) at Exeter, Ontario, Carman, Manitoba, and Lethbridge, Alberta, to evaluate various harvest-aid herbicides in dry bean. Comparison of leaf, pod and stem visual dry down at 4 and 8 d after desiccation application (DAA) indicated that adding a tankmix partner to glyphosate increased visual dry down of leaf, pod and stem 17, 10 and 15% at 4 DAA and 20, 17 and 14% at 8 DAA, respectively. At 8 DAA, glyphosate (450 or 900 g a.e. ha-1), diquat, glufosinate ammonium, carfentrazone-ethyl, flumioxazin and saflufenacil provided 13-58, 65-80, 64-71, 12-34, 36-52 and 41-73% dry down of the dominant weeds (AMARE, AMBEL, CHEAL and SETVI), respectively. Diquat, glufosinate ammonium, carfentrazone-ethyl, flumioxazin and saflufenacil tankmixed with glyphosate (450 or 900 g a.e. ha-1) provided 67-77, 65-71, 22-62, 45-69 and 44-74% weed dry down, respectively. Dry bean yield was not reduced with any of the desiccation treatments. Among desiccant treatments that provided consistent desiccation of dry bean and weeds, saflufenacil had the least environmental impact followed by flumioxazin, glufosinate ammonium and then diquat. Based on this study, diquat, glufosinate ammonium, flumioxazin and saflufenacil alone or in combination with glyphosate (450 or 900 g a.e. ha-1) provide consistent desiccation of weeds and dry bean.
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How this classification was reachedexpand
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) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".