Cellular Protein Trafficking: A New Player in Low-Temperature Response Pathway
Bibliographic record
Abstract
Unlike animals, plants are unable to escape unfavorable conditions, such as extremities of temperature. Among abiotic variables, the temperature is notableas it affects plants from the molecular to the organismal level. Because of global warming, understanding temperature effects on plants is salient today and should be focused not only on rising temperature but also greater variability in temperature that is now besetting the world's natural and agricultural ecosystems. Among the temperature stresses, low-temperature stress is one of the major stresses that limits crop productivity worldwide. Over the years, although substantial progress has been made in understanding low-temperature response mechanisms in plants, the research is more focused on aerial parts of the plants rather than on the root or whole plant, and more efforts have been made in identifying and testing the major regulators of this pathway preferably in the model organism rather than in crop plants. For the low-temperature stress response mechanism, ICE-CBF regulatory pathway turned out to be the solely established pathway, and historically most of the low-temperature research is focused on this single pathway instead of exploring other alternative regulators. In this review, we tried to take an in-depth look at our current understanding of low temperature-mediated plant growth response mechanism and present the recent advancement in cell biological studies that have opened a new horizon for finding promising and potential alternative regulators of the cold stress response pathway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".