The Genome of <i>Apera spica-venti</i>: A Major Grass Weed
Bibliographic record
Abstract
Apera spica-venti (loose silky bent, or common windgrass) is a diploid grass-weed endemic to Europe and north Asia that has spread to the United States and Canada. This species has become a major grass weed in winter cereals, especially in eastern Europe mainly through the evolution of target site and nontarget site resistance mechanisms. The scientific community currently lacks genomic resources to understand herbicide resistance evolution in this plant and therefore resistance is hard to diagnose and treat. To remedy this, we generated two reference haplome assemblies through phased genome assembly. Haplome 1 consists of 37 scaffolds with a total length of 4.06 Gbp and an N50 of 206.5 Mbp, while haplome 2 resulted in 34 scaffolds with a total length of 3.99 Gbp and an N50 of 270.1 Mbp. Both haplomes represent over 87% of the flow cytometry estimated genome size of 4.622 Gbp per 1C. Gene annotation was performed via a modified Maker pipeline resulting in 44,208 and 43,844 genes for haplomes 1 and 2, respectively, and capturing 90% of BUSCO annotated transcripts. Repeat analysis identified greater than 800,000 transposon elements spanning 2.3 Gbp of the genome and an average distance between genes of over 90 kbp. This reference genome addresses the lack of genomic resources and aims to better understand basic weed biology, ecology, and herbicide resistance evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".