Biennial wormwood (Artemisia biennis) competition with soybean (Glycine max)
Bibliographic record
Abstract
Biennial wormwood has become a serious weed of several crops in the northern Great Plains of the United States and Prairie Provinces of Canada. Greenhouse replacement series experiments were conducted to investigate the effects of watering regime (stressed and non-stressed) and nitrogen rate (50, 100, 150, and 200 mg kg-1 of soil) on competition between soybean and biennial wormwood. Soybean height was reduced after 9 weeks of competition with biennial wormwood compared with soybean grown in monoculture, whereas biennial wormwood plants were taller when grown with soybean than in monoculture. The change in plant height indicated that biennial wormwood height was increased due to interspecific competition, whereas soybean height was reduced. When moisture was limited, the relative yield of biennial wormwood was greater than that of soybean, indicating that biennial wormwood was more aggressive than soybean. Soybean growth was unaffected by an increase in nitrogen rate, whereas biennial wormwood fresh weight was 30% greater when the nitrogen rate was increased from 50 to 200 mg kg-1. Biennial wormwood aggressivity tended to increase as the nitrogen rate was increased from 50 to 200 mg kg-1. Overall results suggest that the negative impact of biennial wormwood competition with soybeans under field conditions may increase when soil moisture is limited and nitrogen fertility is increased.
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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".