Survey in the sugarcane expressed sequence tag database (SUCEST) for simple sequence repeats
Bibliographic record
Abstract
Sugarcane microsatellites or simple sequence repeats (SSR) were developed in an economical and practical way by mining EST databases. A survey in the SUCEST (sugarcane EST) database revealed a total of 2005 clusters out of 43,141 containing SSRs. Of these, 8.2% were dinucleotide, 30.5% were trinucleotide, and 61.3% were tetranucleotide repeats. Except for dinucleotides, the CG-rich motif types were the most common. Differences in abundance of trinucleotide motif types were observed between EST-SSRs and those isolated from sugarcane genomic libraries. Among the different cDNA libraries used for EST sequencing, SSRs were more frequent in the ones derived from leaf roll (LR). Twenty-three out of 30 tested SSRs produced scorable polymorphisms in 18 sugarcane commercial clones. These EST-SSRs showed a moderate level of polymorphism with some SSRs producing unique fingerprints. The number of alleles observed among the 18 clones evaluated varied from 2 to 15, with an average of 6.04 alleles/locus. The polymorphism information content (PIC) values ranged from 0.28 to 0.90 with a mean of 0.66. The EST-SSRs screened over both parents (SP 80-180; SP 80-4966) and 6 F1 individuals produced 52 segregating markers that could potentially be used for sugarcane mapping. The EST-SSRs were found in clusters that had significant homology to proteins involved in important metabolic pathways such as sugar biosynthesis, proving that EST-SSRs are a valuable tool for the construction of a functional sugarcane map.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 0.002 |
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".