Temperature‐smart plants: A new horizon with omics‐driven plant breeding
Bibliographic record
Abstract
Abstract The adverse effects of mounting environmental challenges, including extreme temperatures, threaten the global food supply due to their impact on plant growth and productivity. Temperature extremes disrupt plant genetics, leading to significant growth issues and eventually damaging phenotypes. Plants have developed complex signaling networks to respond and tolerate temperature stimuli, including genetic, physiological, biochemical, and molecular adaptations. In recent decades, omics tools and other molecular strategies have rapidly advanced, offering crucial insights and a wealth of information about how plants respond and adapt to stress. This review explores the potential of an integrated omics‐driven approach to understanding how plants adapt and tolerate extreme temperatures. By leveraging cutting‐edge omics methods, including genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, and ionomics, alongside the power of machine learning and speed breeding data, we can revolutionize plant breeding practices. These advanced techniques offer a promising pathway to developing climate‐proof plant varieties that can withstand temperature fluctuations, addressing the increasing global demand for high‐quality food in the face of a changing climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".