Bibliographic record
Abstract
Insect pathogens can be utilized in a variety of pest management approaches, from inundative release to augmentation and classical biological control, and microevolution and the consideration of evolutionary principles can potentially influence the success of all these strategies. Considerable diversity exists in natural entomopathogen populations and this diversity can be either beneficial or detrimental for pest suppression, depending on the pathogen and its mode of competition, and this should be considered in the selection of isolates for biological control. Target hosts can exhibit considerable variation in their susceptibility to entomopathogens, and cases of field-evolved resistance have been documented for Bacillus thuringiensis and baculoviruses. Strong selection, limited pathogen diversity, reduced gene flow, and host plant chemistry are linked to cases of resistance and should be considered when developing resistance management strategies. Pre- and post-release monitoring of microbial control programs have received little attention; however, to date there have been no reports of host-range evolution or long-term negative effects on nontarget hosts. Comparative analyses of pathogen population structure, virulence, and host resistance over time are required to elucidate the evolutionary dynamics of microbial control systems.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".