A global perspective of entomopathogens as microbial biocontrol agents of insect pests
Bibliographic record
Abstract
The growing global population has created a significant demand for both quality and quantity of agricultural products, resulting in a significant increase in the use of agrochemicals such as chemical pesticides to counter insect pests. Consumers, on the other hand, have grown increasingly concerned in recent years about the adverse effects of chemical insecticides on human health and the environment. As a result, researchers worldwide have been compelled to conduct research into alternative crop protection solutions. Biological control through entomopathogens has gained prominence among these alternatives, and several microbial biocontrol agents, including baculoviruses, Bacillus , Beauveria , Steinernema , and Heterorhabditis species, have been tested. Microbial biopesticide products are currently being used to combat specific insects that harm crops. The modes of action of entomopathogens, their advantages and constraints are discussed. An overview of processes for their development are highlighted with examples, as well as the issues that impede their development. Finally, special attention was paid to the gaps identified in this sector and the factors limiting their application particularly with respect to market potential. The future potential of entomopathogens may be affected by changing agricultural systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".