Potential yield loss in sugar beet due to weed interference in the United States and Canada
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".