Arabidopsis research in 2030: Translating the computable plant
Bibliographic record
Abstract
Plants are essential for human survival. Over the past three decades, work with the reference plant Arabidopsis thaliana has significantly advanced plant biology research. One key event was the sequencing of its genome 25 years ago, which fostered many subsequent research technologies and datasets. Arabidopsis has been instrumental in elucidating plant-specific aspects of biology, developing research tools, and translating findings to crop improvement. It not only serves as a model for understanding plant biology and but also biology in other fields, with discoveries in Arabidopsis also having led to applications in human health, including insights into immunity, protein degradation, and circadian rhythms. Arabidopsis research has also fostered the development of tools useful for the wider biological research community, such as optogenetic systems and auxin-based degrons. This 4th Multinational Arabidopsis Steering Committee Roadmap outlines future directions, with emphasis on computational approaches, research support, translation to crops, conference accessibility, coordinated research efforts, climate change mitigation, sustainable production, and fundamental research. Arabidopsis will remain a nexus for discovery, innovation, and application, driving advances in both plant and human biology to the year 2030, and beyond.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".