Host specificity in biological control: insights from opportunistic pathogens
Bibliographic record
Abstract
Host/prey specificity is a significant concern in biological control. It influences the effectiveness of a natural enemy and the risks it might have on non-target organisms. Furthermore, narrow host specificity can be a limiting factor for the commercialization of natural enemies. Given the great diversity in taxonomy and mode of action of natural enemies, host specificity is a highly variable biological trait. This variability can be illustrated by opportunist fungi from the genus Lecanicillium, which have the capacity to exploit a wide range of hosts - from arthropod pests to fungi causing plant diseases - through different modes of action. Processes determining evolutionary trajectories in host specificity are closely linked to the modes of action of the natural enemy. This hypothesis is supported by advances in fungal genomics concerning the identity of genes and biological traits that are required for the evolution of life history strategies and host range. Despite the significance of specificity, we still need to develop a conceptual framework for better understanding of the relationship between specialization and successful biological control. The emergence of opportunistic pathogens and the development of 'omic' technologies offer new opportunities to investigate evolutionary principles and applications of the specificity of biocontrol agents.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".