Fungal‐based bioherbicides for weed control: a myth or a reality?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it