Bibliographic record
Abstract
In order to provide scientific basis for studying the genetic diversity and molecular marker-assisted breeding of loquat, new molecular markers of loquat were developed by analyzing flower transcriptome data. In this study, MIcroSAtellite (MISA) and Blast2GO were used for SSR searching, screening, identification, and analysis of non-redundant Unigenes, and Primer 3 was used for SSR primer design. A search found that 28 617 Unigenes contained 44 622 SSR loci, and the frequency of occurrence of SSR loci was 47.51%, and the total average distribution distance was 4.87 kb. Mononucleotide and dinucleotide repeats were the main repeat types of SSR in the loquat flower transcriptome that the SSR of mononucleotide and dinucleotide repeats accounted for 52.09% and 32.83% of the total SSR, respectively. The dominant repeat motifs were A/T and AG/CT, which accounted for 50.94% and 26.70% of the mononucleotide repeats and dinucleotide respectively, and the 65.79% length of motif was concentrated in 12~20 bp. Gene Ontology (GO) functional classification showed that 28 617 loquat flower transcriptome Unigenes containing SSR loci were enriched in 51 GO terms of the 3 GO categories, and the biological process category involved the most GO terms (21). Primers for 34 301 SSR sequences were successfully designed, with a success rate of 76.87%. It is shown by the analysis that the flower transcriptome SSR loci had high frequency, high distribution density, and high polymorphism potential and could provide abundant repeat types. These could provide scientific basis for studying quantitative trait loci mapping, molecular marker-assisted breeding, genome evolution, genetic diversity analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".