Radish management and grazing effects on weed control and corn response
Bibliographic record
Abstract
Abstract Planting cover crops (CCs) may improve soil health, protect from soil erosion, interrupt disease and pest cycles, minimize weed infestations, provide grazing opportunities, and increase commodity crop yields. In upstate Missouri, field research (2012 and 2013) evaluated the effect of tillage (reduced and no‐till), planting date (nonseeded, early, and late Sept.) of radish (Raphanus sativus L.), grazing (grazed and nongrazed radish) on winter annual weed control, and radish and corn (Zea mays L.) response. The experiment was a split–split plot design with grazed and nongrazed radish as the main plot, tillage as the subplot, and radish planting time as the sub‐subplot with four replicates. The results of this research during 2011–2012 were under extreme drought and 2012–2013 under flash drought conditions. Early planted radish produced greater tuber and foliage mass and reduced winter annual weed dry weights compared to late planting. Grazing in the autumn during dry years did not affect weed control and corn yield. The use of CCs during drought conditions can help alleviate forage shortages for livestock producers, but early planting of radish is critical for autumn grazing. Late‐planted radish treatments resulted in greater corn grain yield than the early planted radish and nonseeded control in 2013; however, there was no impact of planting date and tillage on corn yield in 2012. When planted early, weed control and winter‐kill characteristics of radish make it a favorable CC prior to corn, but radish growth was poor when planted following typical corn harvest dates in upstate Missouri.
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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 | 0.000 |
| 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".