QUANTITATIVE DETECTION OF CAMV- 35S PROMOTER AND T-NOS TERMINATOR IN GENETIC MODIFIED TOMATO FROM IRAQI MARKETS
Bibliographic record
Abstract
Aim of this research was quantitative detection of Increase and decrease in copy number of CaMV-35S promoter and Nos terminator in genetic modified tomato by using Real-time PCR. Twenty four samples of genetic modified tomato seeds were isolated from 84 tomato samples collected from Iraqi markets during the period from December 2016 to January 2017. The experiences were conducted in the Institute of Genetic Engineering, University of Baghdad. Go Taq®qPCR master mix kit supplied by USA Promega Company, three specific primers to CaMV-35S promoter, T-Nos terminator and β-actin housekeeping gene supplied by Canadian Alpha Company were used. To quantitative detection of increase and decrease in copy number of GM tomato samples contain CaMV-35S promoter and T-Nos terminator, comparing with the β-actin (houskeeping gene) using Multiple of Median (MoM) equation. The results showed that the lowest recorded of Ct value was (27.88) for CaMV-35S promoter gave an increase in copy number (1.1766) above the normal limit, while highest recorded of Ct value was (32.67) gave an increase in copy number (1.0350) above the normal limit. The lowest recorded of Ct value was (27.35) for T-Nos terminator gave an increase in copy number (1.1600) above the normal limit, whereas highest recorded of Ct value was (32.82) gave a decrease in copy number (0.9920) under the normal limit.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".