Development of novel <scp>EST</scp> ‐ <scp>SSR</scp> markers for <i>Ephedra sinica</i> (Ephedraceae) by transcriptome database mining
Bibliographic record
Abstract
Premise of the Study Ephedra sinica (Ephedraceae) is a gymnosperm shrub with a wide distribution across Central and Eastern Asia. It is widely cultivated as a medicinal plant, but its wild populations are monitored to determine whether protection is needed. Methods and Results Thirty‐six microsatellite markers, including 11 polymorphic markers, were developed from E. distachya RNA ‐Seq data deposited in the National Center for Biotechology Information db EST database. Among 100 genotyped E. sinica individuals originating from five different population groups, the allele number ranged from three to 22 per locus. Levels of observed and expected heterozygosity ranged from 0 to 0.866 (average 0.176) and 0 to 0.876 (average 0.491), respectively. Allelic polymorphism information content ranged from 0.000 to 0.847 (average 0.333). Cross‐species amplifications were successfully conducted with two related Ephedra species for all 11 di‐ or trinucleotide simple sequence repeats. Conclusions This study provides the first set of microsatellite markers for genetic monitoring and surveying of this medicinal plant.
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) | 0.001 | 0.001 |
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".