Genomic Variation Detection Analysis of 3 Self-pruning Tomato Lines Based on Resequencing
Bibliographic record
Abstract
A close relationship was indicated between the yield of self-pruning tomato varieties and the number of main stem flowers. There were many reports on the genes controlling the traits of inflorescence sealing, but few on the related studies controlling the nodes of inflorescence sealing. To study the genetic variation between different self-pruning tomatoes stem and inflorescence segments, the candidate genes involved in the regulation of inflorescence segments were screened out. In this experiment, the variation of 3 tomatoes strains GXF, AXF, and 815 were detected by resequencing technology. The results showed that a total of 5 968 501 SNPs and 485 114 Indel were detected in the three samples, and a total of 33 473 gene mutations were detected after comparison with the reference genome. The GO and KEGG databases were used to compare the mutations in the CDS region, and it was found that they mainly focused on basic metabolism and zein biosynthesis. Through genome sequence alignment analysis, 16 genes that may be involved in the regulation of the terminal segments of the tomato main stem were screened. The results confirmed the mutation locus information of related genes and laid a foundation for further research on the genetic mechanism of inflorescence positions of self-pruning tomatoes.
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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 | 0.001 | 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 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".