Common Ragweed (<i>Ambrosia artemisiifolia</i> L.) Interference with Soybean in Nebraska
Bibliographic record
Abstract
Core Ideas Interference of common ragweed in soybean was driven by competition for light. The leaf area ratio model at R6 growth stage was a robust predictor of yield loss. Twelve common ragweed m−1 row length resulted in 80‐95% soybean yield loss. Common ragweed is a competitive weed in soybean fields in north central United States and eastern Canada. The effect of available soil water on the competitiveness of common ragweed in soybean hasn’t been determined. A field study was conducted in 2015 and 2016 in Nebraska to assess common ragweed interference in soybean as affected by available soil water and common ragweed density. The experiment was arranged in a split‐plot design with irrigation level as main plots and common ragweed density as subplots. Periodic crop and weed leaf area index (LAI) and aboveground biomass were sampled and soybean yield was harvested. A model set was constructed using the rectangular hyperbolic and leaf area ratio models and the best model for predicting yield loss among years was identified using the information‐theoretic criterion. No effect of irrigation level on soybean yield was detected due to near adequate rainfall during the study. Common ragweed densities of 2, 6, and 12 m−1 row resulted in soybean yield losses of 76, 91, and 95% in 2015 and 40, 66, and 80% in 2016, respectively. The leaf area ratio model using relative leaf area at the R6 growth stage of soybean best fit the data. The leaf area ratio model includes both soybean and common ragweed leaf area and, therefore, is putatively more effective at accounting for variation in competition for light than density among years. Results of this study suggest that soybean‐common ragweed interference resulted in substantial soybean yield loss when competing for light.
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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 | 0.001 | 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".