Transcriptome comparison of winter and spring wheat responding to low temperature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it