Efficacy of natural herbicides on dandelion (<i>Taraxacum officinale</i> G.H. Weber ex Wiggers) and white clover (<i>Trifolium repens</i> L.) populations
Bibliographic record
Abstract
Abstract Perennial broadleaves such as dandelion (Taraxacum officinale G.H. Weber ex Wiggers) and white clover (Trifolium repens L.) are known to be pervasive weeds in stands of maintained turfgrasses. The use of synthetic herbicides is the most common and effective method of control for these weeds. As pesticide use in European countries, Canada, and the United States is becoming more scrutinized, identification of alternative weed control options may be necessary. However, few organic or natural weed control products exist. A field study was conducted to evaluate the efficacy of various fertilizers and organic and bio‐herbicides including chelated iron, ammonium nanonate, citrus oil, acetic acid, and sodium chloride on dandelion and white clover control as compared to that of two synthetic herbicides containing 2,4‐dichlorophenoxy‐acetic acid (2,4‐D), mecoprop‐p (MCPP), and dicamba. Injury to perennial ryegrass (Lolium perenne L.) was also evaluated with the objective of determining which products effectively suppressed weed populations while imposing minimal injury to desirable turfgrasses. Chelated iron was effective in controlling dandelion and white clover populations equal to that of both synthetic herbicides with minimal injury to turfgrass. Other organic and bio‐herbicide treatments provided some control of both weed populations but generally were too injurious to the turfgrass. Fertility treatments and citrus oil did not reduce populations of either weed. This research indicates that some natural products currently on the market may serve as effective alternatives to synthetic herbicides. This information will be beneficial to homeowners and turfgrass managers controlling weed populations in geographic areas with restricted pesticide use or where control with organic products is desired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".