Fungal‐based bioherbicides for weed control: a myth or a reality?
Bibliographic record
Abstract
Summary The use of bioherbicides containing fungal active ingredients or natural fungal molecules is one of the possible solutions to reduce the use of chemical products. This paper focuses on studies of bioherbicides, including both living fungi and natural fungal molecules, published in the last 45 years, and their associated weed targets; current problems in the development of bioherbicides are also discussed. Bibliometric methods based on the Web of Science database were used to analyse relevant articles published between 1973 and 2018. Overall analysis suggested that interest in bioherbicides extends over the preceding thirty years, when many potential microorganisms and natural fungal molecules were proposed. Furthermore, analysis of about 229 articles indicated an encouraging exploitable potential, although there is a real gap between the number of experimental studies and the small number of products currently on the market. A dozen fungal‐based bioherbicides are on the market in the United States and Canada, while countries, such as China and South Africa, have one, and none is available in Europe. The active ingredients in these bioherbicides are living fungi, but no fungal molecule‐based product is thus far on the market. Reasons for this gap include production hurdles, formulation process, ecological fitness, duration of herbicidal effects, and costly and time‐consuming registration procedures. However, it is clear that analysis of fungus–plant interactions provides a promising source of bioherbicides that may be applied to appropriate cropping systems for environment‐friendly, sustainable weed control.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".