Prospects for the management of insect pests in the genomic era.
Bibliographic record
Abstract
The rapid development of DNA sequencing technology and bioinformatics has resulted in an increase in genomic studies of insects. Better knowledge of population genetics and evolutionary ecology has allowed better understanding of the local/global adaptation and infestation mechanisms of key agricultural pests, thereby providing novel strategies and approaches for the implementation of integrated pest management(IMP) in a safe, cost-effective and sustainable manner. Genome information has been released for approximately 30 insect species annually over the past two years. Genomic-related studies of agricultural insect herbivores generate a great deal of important data, and reveal the mechanisms underlying the genetic variation, strategic adaptation, and the population dynamics, of these pests. Such studies, in conjunction with the general principles and methods of classical genetics, ecology and evolution, have facilitated the development of novel technology and tools for pest management. This article is an overview of progress in research on insect genomic researches, co-evolution, the interactive mechanisms between plants and herbivores, molecular mechanisms of insect immunity and resistance to insecticides, as well as the development of new techniques for pest management. We believe that this review updates existing information and provides sound prospects for improving the strategies and tactics currently employed in ecologically-based pest management.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".