Transcriptome comparison of winter and spring wheat responding to low temperature
Bibliographic record
Abstract
Freezing tolerance in plants is a complex trait that occurs in many plant species during growth at low, nonfreezing temperatures, a process known as cold acclimation. This process is regulated by a multigenic system expressing broad variation in the degree of freezing tolerance among wheat cultivars. Microarray analysis is a powerful and rapid approach to gene discovery. In species such as wheat, for which large scale mutant screening and transgenic studies are not currently practical, genotype comparison by this methodology represents an essential approach to identifying key genes in the acquisition of freezing tolerance. A microarray was constructed with PCR amplified cDNA inserts from 1184 wheat expressed sequence tags (ESTs) that represent 947 genes. Gene expression during cold acclimation was compared in 2 cultivars with marked differences in freezing tolerance. Transcript levels of more than 300 genes were altered by cold. Among these, 65 genes were regulated differently between the 2 cultivars for at least 1 time point. These include genes that encode potential regulatory proteins and proteins that act in plant metabolism, including protein kinases, putative transcription factors, Ca2+ binding proteins, a Golgi localized protein, an inorganic pyrophosphatase, a cell wall associated hydrolase, and proteins involved in photosynthesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".