Treatment Analogous to Seasonal Change Demonstrates the Integration of Cold Responses in <i>Brachypodium distachyon</i>
Bibliographic record
Abstract
Anthropogenic climate change precipitates the need to understand plant adaptation. Crucial in temperate climates, adaptation to winter is characterized by cold acclimation and vernalization, which respectively lead to freezing tolerance and flowering competence. However, the progression of these responses during fall and their interaction with plant development are not completely understood. By identifying key seasonal cues found in the native range of the cereal model Brachypodium distachyon, we designed a diurnal-freezing treatment (DF) that emulates summer-to-winter change. DF induced unique cold acclimation and vernalization responses characterized by low VERNALIZATION1 (VRN1) expression. Flowering under DF is characterized by an up-regulation of FLOWERING LOCUS T (FT) postvernalization independent of VRN1 expression. DF, while conferring flowering competence, favors a high tolerance to freezing and the development of a winter-hardy plant structure. The findings of this study highlight the contribution of phenotypic plasticity to freezing tolerance and demonstrate the integration of key morphological, physiological, and molecular responses in cold adaptation. The results suggest a fundamental role for VRN1 in regulating cold acclimation, vernalization, and morphological development in B. distachyon. This study also establishes the usefulness of reproducing natural cues in laboratory settings.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".