Emergence timing and control of dandelion (<i>Taraxacum officinale</i>) in spring wheat
Bibliographic record
Abstract
In the northern region of the northern Great Plains of North America, the relative abundance of dandelion in field crops has increased over the past two decades, and farmers need information to help them to better manage this species and slow its spread. A study was conducted to determine the emergence timing of dandelion from both rootstock and seed, and to investigate the efficacy of preseeding (spring) versus postharvest (autumn) herbicide treatments on dandelion in spring wheat fields. Emergence of dandelion plants from rootstock was very early (mean time to 50% emergence [E50] of 430 growing degree days [GDD] Tbase0 C), while seedling emergence was much later (mean E50of 980 GDD). Dandelion does not have a persistent seed bank, and seedling emergence occurred only after dandelion plants arising from rootstock flowered and shed seed. Herbicide treatments that included glyphosate plus florasulam, glyphosate plus tribenuron, or higher rates of glyphosate alone (≥675 g ae ha−1), provided high levels of dandelion control. Autumn herbicide applications were more effective than spring applications for reducing dandelion infestation levels (both aboveground biomass and density). Autumn herbicide applications came after peak emergence timing for dandelion plants emerging both from rootstock and from seed. Because dandelion is a simple perennial, population spread must be limited by controlling seedlings. Autumn herbicide applications provide control of dandelion seedlings and therefore, should limit dandelion population spread.
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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) | 0.000 | 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".