Comparison of sequential preemergence-postemergence and postemergence-alone weed management strategies for red beet (<i>Beta vulgaris</i> L.)
Bibliographic record
Abstract
Robinson, D. E., McNaughton, K. E. and Bilyea, D. 2013. Comparison of sequential preemergence-postemergence and postemergence-alone weed management strategies during critical period of red beet ( Beta vulgaris L.). Can. J. Plant Sci. 93: 863–870. Weed management during the first 4 wk after red beet emergence is critical to prevent yield loss. The purpose of this research was to compare weed control and red beet tolerance of postemergence (POST) treatments alone (full, split- and micro-rates of pyrazon plus triflusulfuron) with sequential preemergence (PRE) applications of s-metolachlor followed by these POST herbicide treatments. The experiment was carried out for 4 yr as a randomized complete block with four replications. Of the POST alone treatments examined, control of velvetleaf (Abutilon theophrasti Medik.), redroot pigweed (Amaranthus retroflexus L.), common lambsquarters (Chenopodium album L.) and green foxtail [Setaria viridis L. (Beauv.)] was 98, 86, 97 and 62%, respectively. Much more consistent control of all species (i.e., greater than 96%) was measured where the sequential treatment of s-metolachlor was applied PRE followed by POST split- or micro-rates in all study years. Red beet density and soluble solid levels were not different than the weed-free check in any of the POST or sequential treatments. However, red beet yield in the POST single and split-rate treatments alone ranged from 9500 to 12100 kg ha −1 , and was significantly less than in the weed-free check (19400 kg ha −1 ). Yields in the sequential treatments were comparable to the weed-free check. The results of this study confirm that a sequential treatment of PRE s-metolachlor followed by POST split- and micro-rate applications of pyrazon plus triflusulfuron can be safely applied to red beet and control annual weed species.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".