Development of Microsatellite Markers Derived from Expressed Sequence Tags of Polyporales for Genetic Diversity Analysis of Endangered<i>Polyporus umbellatus</i>
Bibliographic record
Abstract
A large scale of EST sequences of Polyporales was screened in this investigation in order to identify EST-SSR markers for various applications. The distribution of EST sequences and SSRs in five families of Polyporales was analyzed, respectively. Mononucleotide was the most abundant type, followed by trinucleotide. Among five families, Ganodermataceae occupied the most SSR markers, followed by Coriolaceae. Functional prediction of SSR marker-containing EST sequences in Ganoderma lucidum obtained three main groups, namely, cellular component, biological process, and molecular function. Thirty EST-SSR primers were designed to evaluate the genetic diversity of 13 natural Polyporus umbellatus accessions. Twenty one EST-SSRs were polymorphic with average PIC value of 0.33 and transferability rate of 71%. These 13 P. umbellatus accessions showed relatively high genetic diversity. The expected heterozygosity, Nei's gene diversity, and Shannon information index were 0.41, 0.39, and 0.57, respectively. Both UPGMA dendrogram and principal coordinate analysis (PCA) showed the same cluster result that divided the 13 accessions into three or four groups.
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 | 0.001 | 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 | 0.000 |
| 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".