Assessing Polymorphism Information Content (PIC) Using SSR Molecular Markers on Local Species of Citrullus Colocynthis. Case Study: Iran, Sistan-Balouchestan Province
Bibliographic record
Abstract
Studying polymorphism information content in plants, particularly local plants which are rich in genetic variety, can play an efficient role in building genetic bank and species’ breeding. Iranian colocynth is highly important in terms of medicinal and therapeutic traits and hence, it needs assessing the polymorphism information content. In present research, simple sequence repeat (SSR) markers are used. 32 samples are randomly collected from local accumulations in 8 regions of Sistan-Balouchestan province. DNA extraction from each sample was done using Cetyltrimethylammonium Bromide. 10 primers were designed and used in this study. Polymerase chain reaction was done using extracted DNA and ten primers. To analyze genetic data, NTSYS pc ver.2/2, Genalex 6.5 and XLSATAT software were used. Regarding data analysis of respective values and taking this fact into account that7 out of 10 used primers showed polymorphism information content in respective samples, their classification was not possible and hence, categorization of similar shapes was not performed due to highly genetic diversity of this species and, also, each value had a different shape.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".