INTEGRATED PEST MANAGEMENT AND ENTOMOPATHOGENIC FUNGAL BIOTECHNOLOGY IN THE LATIN AMERICAS: I-OPPORTUNITIES IN A GLOBALAGRICULTURE
Bibliographic record
Abstract
Entomopathogenic fungi (EPF) have been used as insect biocontrol agents separately and in integrated pest management (1PM) worldwide including in the Latin Americas. The proper use of these agents along with other elements and concepts of 1PM has significant impact on the provision of crops. fruits and other agricultura! products Entomopathogenic fungi can play a significant role in the preservation of the natural delicate balance of beneficia! biota and their active spreading from the surrounding natural habitats, such as the tropical rain forest, the humid cloudy forest in the Andes and other important ecosystems, to the agricultura} lands in Central and South America. Of ali known EPF, there are only about half a dozen for which prerequisite aspects of fungal biotechnology for industrial R and D are in place. Because of its precedent setting leadership in the use ofEPF in 1PM and agroecological practices, LatinAmerica stands to gain irnrnensely here and more so if the concept of rational design of bioinsecticides (RADBIO) is employed. The future prospects will depend on a balance of discovery research coupled to a strong and dependable industrialization effort to develop the framework for illustrating public acceptance, commercialization potential and widespread use of EPF in 1PM. The potential for the next generation of mycoinsecticides is now loorning on the horizon.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".