Editorial: Integrated omics approaches to accelerate plant improvement
Bibliographic record
Abstract
In the near future, the human population is estimated to reach 10 billion, causing an increased demand for food, feed, and other plant products. To meet these demands, it is crucial to accelerate the genetic improvement of key plant species. To increase the quality and quantity of plant products, advances in cultivation practices and or advances in genetics are necessary. Historically, traditional methods have concentrated on exploring the rearrangement of genetic variability through selection and in-field evaluations to enhance the value of complex traits. However, these traits are naturally influenced by numerous factors across the different levels of the central dogma of molecular biology. Therefore, integrating data from various 'omics' fields-including genomics, transcriptomics, and metabolomics-can significantly contribute to a deeper understanding and improvement of these complex traits. Recent advancements in the utilization of large datasets have shown promising results in accurately predicting desirable traits. These efforts involve studying various 'omics' levels, from molecular biology to agricultural environments. However, the scale and complexity of these datasets present challenges that necessitate novel insights and analytical methods to facilitate informed decision-making in plant breeding.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".