Overexpression of miR156 and Silencing <i>SPL6RNAi</i> and <i>SPL13RNAi</i> Genes in <i>Medicago sativa</i> on the Changes of Carbohydrate Physiochemical, Fermentation, and Nutritional Profiles
Bibliographic record
Abstract
This study aimed to explore the comparative effects of overexpressing miR156 with individually silencing SPL6RNAi and SPL13RNAi genes on carbohydrate physiochemical, fermentation, and nutritional profiles of alfalfa ( Medicago sativa ). Three sub-genotypes of miR156 overexpressed (miR156 OE), SPL6RNAi, and SPL13RNAi grown with the wild type (WT) in a greenhouse were harvested 3 times at an early vegetative stage. Samples were freeze-dried, ground, and analyzed for carbohydrate nutritional profiles in terms of chemical composition, CNCPS fractions, energetic values, in vitro degradation, and fermentation characteristics. Results showed that miR156 OE had lower fiber and higher energy compared to all other genotypes. Moreover, miR156 OE had higher starch compared to SPL13RNAi and higher DM degradation compared to WT and SPL13RNAi. In conclusion, overexpression of miR156 decreased the fiber content of alfalfa but increased energy and DM degradation. SPL6RNAi was more similar to miR156 OE alfalfa in chemical composition and degradation, indicating that the SPL6RNAi gene plays an important role in the miR156 overexpression event.
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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) | 0.001 | 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".