Genotype data of Anoplophora Glabripennis from invasive populations in North America and native population in Asia
Bibliographic record
Abstract
This dataset provides genomic resources for the invasive Asian longhorned beetle (Anoplophora glabripennis Motschulsky, ALB), a significant pest threatening global forest ecosystems. It includes 2,768 genome-wide single nucleotide polymorphisms (SNPs) derived from invasive ALB populations in North America, enabling the study of genetic variation, invasion history, and population dynamics. The dataset is structured to support analyses of genetic bottlenecks, population expansions, and secondary spread patterns, offering insights into multiple independent introductions from the native range. The dataset is organized into genotype matrices and metadata, including sample locations, collection dates, and population identifiers. It is reusable for studies on invasion biology, biosurveillance, and biosecurity, providing a foundation for tracing the origins of intercepted individuals and informing pest management strategies. Legal and ethical considerations include compliance with data-sharing policies and restrictions on the use of genetic data for invasive species management. This resource is designed to enhance genome-based biosurveillance tools, supporting regulatory agencies in strengthening biosecurity measures against ALB and other invasive pests.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".