Development and Characterization of EST-SSR Markers from NCBI and cDNA Library in Cultivated Peanut (<i>Arachis hypogaea L.</i>)
Bibliographic record
Abstract
86 132 ESTs downloaded from GenBank in NCBI and 12 501 ESTs from cDNA library constructed by high-oil linoleic acid accession E12 were analysed. After the preprocession, there were 18 051 singletons and 9 972 contigs in the GenBank of NCBI and cDNA library. Totally 3 104 SSR locis had been screened by MISA software, accounting for 11.08% for these non-redundant ESTs. All SSR locis are divided into di-nucleotide, thi-nucleotide, tetra-nucleotide, penta-nucleotide, hexa-nucleotide and multi-nucleotide etc., and thi-nucleotide motif is the most motifs and the frequency was 43.0% and 56.8% in NCBI and cDNA libraray, respectively. The number of di- and penta-nucleotide motifs were second and third in all motifs. And the hexa-nucleotide was the least motif both in NCBI and cDNA library. In all repeat motifs nucleotide, AG/TC was the most motifs and accounted for 8.65% and 13.42% in NCBI and cDNA library respectively. Among the tri-nucleotide repeats, CTT/GAA was the most frequent motif, accounting for 6.7% and 13.42%, respectively. The repeat unit number of SSR locis is between 4 and 51.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".