Weed-Competitive Ability of Spring and Winter Cereals in the Northern Great Plains
Bibliographic record
Abstract
The inclusion of winter cereals in spring-annual rotations in the northern Great Plains may reduce weed populations and herbicide requirements. A broad range of spring and winter cereals were compared for ability to suppress weeds and maximize grain yield at Lacombe (2002 to 2005) and Lethbridge (2003 to 2005), Alberta, Canada. High seeding rates (≥ 400 seeds/m2) were used in all years to maximize crop competitive ability. Spring cereals achieved high crop-plant densities (> 250 plants/m2) at most sites, but winter cereals had lower plant densities due to winterkill, particularly at Lethbridge in 2004. All winter cereals and spring barley were highly effective at reducing weed biomass at Lacombe for the first 3 yr of the study. Weed suppression was less consistently affected by winter cereals in the last year at Lacombe and at Lethbridge, primarily due to poor winter survival. Grain yields were highest for spring triticale and least for spring wheat at Lacombe, with winter cereals intermediate. At Lethbridge, winter cereals had higher grain yields in 2003 whereas spring cereals had higher yields in 2004 and 2005. Winter cereals were generally more effective at suppressing weed growth than spring cereals if a good crop stand was established, but overlap in weed-competitive ability among cultivars was considerable. This information will be used to enhance the sustainable production of winter and spring cereals in traditional and nontraditional agro-ecological zones.
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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".