Farmer Perceptions of Weed Problems in Corn and Soybean Rotation Systems
Bibliographic record
Abstract
Corn and soybean growers across Indiana were surveyed in 2003 to determine their perceptions of the importance of weed problems in various crop rotations. Growers were asked to list the three most problematic weeds in the following rotation systems: soybean and corn planted in alternate years (SC) and corn (CC) or soybean (SS) planted to the same field for 2 or more years. Although some summer annuals and perennials (common lambsquarters, Canada thistle, and common cocklebur) and winter annuals (chickweed and henbit) were considered problematic by at least 10% of growers in all three systems, there were differences among systems in the relative importance of weed species. Giant ragweed was considered problematic by at least 30% of SC and CC growers but by less than 10% of SS growers. Horseweed was listed as a problematic summer annual by 13% of SS growers but by only 3% of CC growers. Purple deadnettle was listed by 15% of CC growers but by less than 6% of SC and SS growers. Perennial dicots were more problematic in SS than in CC. Annual and perennial grasses were more problematic in CC than in SC or SS. Despite these differences, the results of this survey suggest that the cumulative effect of weed management practices in corn and soybean rotation systems in Indiana has been the promotion of larger seeded, broadleaf, summer annual species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".