Transcriptome characterization and screening of molecular markers in ecologically important Himalayan species (<i>Rhododendron arboreum</i>)
Bibliographic record
Abstract
Rhododendron arboreum is an ecologically prominent species, which also lends commercial and medicinal benefits in the form of palatable juices and useful herbal drugs. Local abundance and survival of the species under a highly fluctuating climate make it an ideal model for genetic structure and functional analysis. However, a lack of genomic data has hampered additional research. In the present study, cDNA libraries from floral and foliar tissues of the species were sequenced to provide a foundation for understanding the functional aspects of the genome and to construct an enriched repository that will promote genomics studies in the genera. Illumina's platform facilitated the generation of ∼100 million high-quality paired-end reads. De novo assembly, clustering, and filtering out of shorter transcripts predicted 113 167 non-redundant transcripts with an average length of 1164.6 bases. Of these, 71 961 transcripts were categorized based on functional annotations in the Gene Ontology database, whereby 5710 were grouped into 141 pathways and 23 746 encoded for different transcription factors. Transcriptome screening further identified 35 419 microsatellite regions, of which, 43 polymorphic loci were characterized on 30 genotypes. Seven hundred and nineteen transcripts had 811 high-quality single-nucleotide polymorphic variants with a minimum coverage of 10, a total score of 20, and SNP% of 50.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".