Evolution of Weediness and Invasiveness: Charting the Course for Weed Genomics
Bibliographic record
Abstract
The genetic basis of weedy and invasive traits and their evolution remain poorly understood, but genomic approaches offer tremendous promise for elucidating these important features of weed biology. However, the genomic tools and resources available for weed research are currently meager compared with those available for many crops. Because genomic methodologies are becoming increasingly accessible and less expensive, the time is ripe for weed scientists to incorporate these methods into their research programs. One example is next-generation sequencing technology, which has the advantage of enhancing the sequencing output from the transcriptome of a weedy plant at a reduced cost. Successful implementation of these approaches will require collaborative efforts that focus resources on common goals and bring together expertise in weed science, molecular biology, plant physiology, and bioinformatics. We outline how these large-scale genomic programs can aid both our understanding of the biology of weedy and invasive plants and our success at managing these species in agriculture. The judicious selection of species for developing weed genomics programs is needed, and we offer up choices, but noArabidopsis-like model species exists in the world of weeds. We outline the roadmap for creating a powerful synergy of weed science and genomics, given well-placed effort and resources.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".