Potential yield loss in sugar beet due to weed interference in the United States and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The objective of this WSSA Weed Loss Committee report is to provide quantitative data on the potential yield loss in sugar beet due to weed interference from the major sugar beet growing areas of the United States and Canada. Researchers and extension specialists who conducted research on weed control in sugar beet in the United States and Canada provided quantitative data on sugar beet yield loss due to weed interference in their regions. Specifically, data were requested from weed control studies in sugar beet from up to 10 individual studies per calendar year over a 15-yr period between 2002 and 2017. Data collected indicated that if weeds are left uncontrolled under optimal agronomic practices, growers in Idaho, Michigan, Minnesota, Montana, Nebraska, North Dakota, Ontario, Oregon, and Wyoming would potentially lose an average of 79%, 61%, 66%, 68%, 63%, 75%, 83%, 78%, and 77% of the sugar beet yield. The corresponding monetary loss would be approximately US$234, US$122, US$369, US$43, US$40, US$211, US$12, US$14, and US$32 million, respectively. The average yield loss due to weed interference for the primary sugar beet growing areas of North America was estimated to be 70%. Thus, if weeds are not controlled, growers in the United States would lose approximately 22.4 million tonnes of sugar beet yield valued at approximately US$1.25 billion, and growers in Canada would lose approximately 0.5 million tonnes of sugar beet yield valued at approximately US$25 million. The high return on investment in weed management highlights the importance of continued weed science research for sustaining high crop yield and profitability of sugar beet production in North America.
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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 | 0.000 |
| Open science | 0.000 | 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 it