Evolution and the microbial control of insects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it