Potential yield loss in grain sorghum (<i>Sorghum bicolor</i>) with weed interference in the United States
Bibliographic record
Abstract
Abstract Potential yield losses in grain sorghum due to weed interference based on quantitative data from the major grain sorghum-growing areas of the United States are reported by the WSSA Weed Loss Committee. Weed scientists and extension specialists who researched weed control in grain sorghum provided data on grain sorghum yield loss due to weed interference in their region. Data were requested from up to 10 individual experiments per calendar year over 10 yr between 2007 and 2016. Based on the summarized information, farmers in Arkansas, Kansas, Missouri, Nebraska, South Dakota, and Texas would potentially lose an average of 37%, 38%, 30%, 56%, 61%, and 60% of their grain sorghum yield with no weed control, and have a corresponding annual monetary loss of US $19 million, 302 million, 7 million, 32 million, 25 million, and 314 million, respectively. The overall average yield loss due to weed interference was estimated to be 47% for this grain sorghum-growing region. Thus, US farmers would lose approximately 5,700 million kg of grain sorghum valued at approximately US $953 million annually if weeds are not controlled. With each dollar invested in weed management (based on estimated weed control cost of US $100 ha−1), there would be a return of US $3.80, highlighting the return on investment in weed management and the importance of continued weed science research for sustaining high grain sorghum yield and profitability in the United States.
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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 | 0.001 |
| 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".