Impact of cultivars and herbicides on weed management in alfalfa
Bibliographic record
Abstract
Weeds, especially dandelion (Taraxacum officinale Weber in F.H. Wigg.), tend to infest a forage alfalfa (Medicago sativa L.) stand 2 to 4 yr after establishment. To develop better weed management systems, experiments were conducted at Lethbridge, Alberta, from 1995 to 2002 and Creston, British Columbia, from 1998 to 2001, which included the alfalfa cultivars Beaver (standard type) and AC Blue J (Flemish type) and annual applications of metribuzin and hexazinone. These herbicides are registered for weed control in irrigated alfalfa in Alberta and alfalfa grown for seed. In addition, two sulfonylurea herbicides, metsulfuron and sulfosulfuron, and glyphosate were included. All of the herbicides except glyphosate controlled or suppressed dandelion and mustard family weeds. Metsulfuron at 5 g a.i. ha -1 almost completely controlled dandelion at both locations. However, after metsulfuron application at Lethbridge, dandelion was replaced with an infestation of downy brome, which is unpalatable for cattle. None of the herbicides increased total forage (alfalfa + weed) yield, and in some instances herbicides reduced forage quality by causing a shift from a palatable to an unpalatable weed species. However, it was observed that AC Blue J consistently yielded more than Beaver, and weed biomass was consistently less in the higher-yielding cultivar. AC Blue J was developed primarily for the irrigated area in southern Alberta and for southern British Columbia. Therefore, additional experiments should be conducted to determine which alfalfa cultivars have the greatest ability to compete with weeds in other regions of western Canada. Key words: Alfalfa yield, dandelion, forage quality, weed control
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".