Formulations of Biopesticides
Bibliographic record
Abstract
A large number of factors can potentially affect the economic feasibility of any given biological control product. These include the impact on the target pest, market size and spectrum of pests affected by the biocontrol agent, vari ability of field performance, costs of production, and a number of technologi cal challenges, including fermentation, formulation, and delivery systems ( 1 – 42 ). Selection of the appropriate formulations that can improve product sta bility and viability may reduce inconsistency of field performance of many potential biological control agents (( 2 ), 5 , 6 ). It has been indicated that slow progress in research on formulation and delivery systems is a major hurdle to the development of biopesticide products (( 1 ),( 7 )). This chapter summarizes the efforts and successes toward formulation of biocontrol products for use against diseases (biofungicides), weeds (bioherbicides), and insect pests (bioinsecticides). The discussion emphasizes the use of bacteria, fungi, and viruses as the agents. Information on formulation of other important biocontrol agents, such as nematodes, can be found elsewhere ( 8 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".