Achievements, Developments and Future Challenges in the Field of Bioherbicides for Weed Control: A Global Review
Bibliographic record
Abstract
The intrusion of weeds into fertile areas has resulted in significant global economic and environmental impacts on agricultural production systems and native ecosystems, hence without ongoing and repeated management actions, the maintenance or restoration of these systems will become increasingly challenging. The establishment of herbicide resistance in many species and unwanted pollution caused by synthetic herbicides has ushered in the need for alternative, eco-friendly sustainable management strategies, such as the use of bioherbicides. Of the array of bioherbicides currently available, the most successful products appear to be sourced from fungi (mycoherbicides), with at least 16 products being developed for commercial use globally. Over the last few decades, bioherbicides sourced from bacteria and plant extracts (such as allelochemicals and essential oils), together with viruses, have also shown marked success in controlling various weeds. Despite this encouraging trend, ongoing research is still required for these compounds to be economically viable and successful in the long term. It is apparent that more focused research is required for (i) the improvement of the commercialisation processes, including the cost-effectiveness and scale of production of these materials; (ii) the discovery of new production sources, such as bacteria, fungi, plants or viruses and (iii) the understanding of the environmental influence on the efficacy of these compounds, such as atmospheric CO2, humidity, soil water stress, temperature and UV radiation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".