Potential potato yield loss from weed interference in the United States and Canada
Bibliographic record
Abstract
Abstract Potato is the third most important staple food crop globally following rice and wheat. In the United States, potato is grown on approximately 410,000 ha with a farm-gate value of US$1,032 million. In Canada, potato is grown on approximately 134,000 ha with a farm-gate value of US$235 million. The objective of this manuscript, compiled by the Weed Science Society of America Weed Loss Committee, was to estimate potato yield loss caused by weed interference. Potato yield data from weedy and weed-free plots (or plots with >95% weed control) was obtained from researchers working on weed management in potato in the United States and Canada or from published manuscripts from 2000 to 2018. Potato yield loss from weed interference was 12% to 61% when no weed management tactics were implemented. The average yield loss for all states/provinces (where data was obtained) due to weed interference was 44%. Weed interference would cause a farm-gate loss of approximately US$465 million and US$61 in the United States and Canada, respectively, if weeds are not controlled. These results indicate that weed management is critical for successful potato production, and that an ongoing need for research exists on weed management in this crop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".