Genomic Analysis of Earwigs and Their Ecological Adaptation: From Genome Assembly to Molecular Mechanisms of Environmental Adaptation
Bibliographic record
Abstract
The earwig, Forficula auricularia , serves as a significant model organism for studying maternal care, sexual selection, sociality, and host-parasite interactions. Despite its importance, genetic research on this species has been limited due to a lack of comprehensive genomic resources. This study synthesizes recent advancements in the genomic analysis of earwigs, focusing on genome assembly, annotation, and the molecular mechanisms underlying their ecological adaptation. High-quality genome assemblies have been developed using advanced sequencing technologies, revealing extensive genetic diversity and adaptive traits. These genomic resources have facilitated the identification of key genes and pathways involved in environmental adaptation, including responses to temperature, humidity, and other ecological stressors. Comparative genomic studies have further elucidated the evolutionary processes and genetic architecture that enable earwigs to thrive in diverse environments. This study highlights the potential of genomic tools to enhance the understanding of earwig biology and their adaptive strategies, providing a valuable foundation for future research in evolutionary ecology and conservation genetics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".