Bibliographic record
Abstract
MicroRNAs (miRNAs) are small (∼21-nucleotides) non-coding RNAs found in plant and animals. MiRNAs function as critical post-transcriptional regulators of gene expression by binding to complementary sequences in their target mRNAs, leading to mRNA destabilization and translational inhibition. Plant miRNAs have some distinct characteristics compared to their animal counterparts, including greater evolutionary conservation and unique miRNA processing methods. The lifecycle of a plant begins with embryogenesis and progresses through seed germination, vegetative growth, reproductive growth, flowering and fruiting, and finally senescence and death. MiRNAs participate in the transformation of plant growth and development and directly monitor progression of these processes and the expression of certain morphological characteristics by regulating transcription factor genes involved in cell growth and differentiation. In woody plants, a large and rapidly increasing number of miRNAs have been identified, but their biological functions are largely unknown. In this review, we summarize the progress of miRNA research in woody plants to date. In particular, we discuss the potential roles of these miRNAs in growth, development, and biotic and abiotic stresses responses in woody plants.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".