DNA extraction technique for different rice varieties grown in Sri Lanka
Bibliographic record
Abstract
Extraction of DNA is very important nowadays in bio-molecular researches. Extracted DNA should be purified and the quality of DNA should also be very high. The objective of the study was to develop a simple efficient method to isolate DNA from the rice varieties in an open laboratory environment, and to eliminate the usage of expensive chemicals and tools. The DNA extraction methods developed by the DNeasy plant kit method supplied by QIAGEN, Cheng et al., Doyle et al. and Michiels et al. were applied to five different rice varieties grown in different parts of Sri Lanka. Based on the quantity and quality of the extracted DNA tested by measuring the absorbance of DNA at 260 nm using Nanodrop® ND-1000 spectrophotometer and measuring the ratio of A260 / A280 and gel running on agarose, the efficiency of the extraction method chosen varied among rice varieties. Among the methods used, the methods introduced by DNeasy plant kit method supplied by QIAGEN and Cheng et al, yielded good and amplifiable quality DNA with satisfactory concentration for all the rice varieties tested. Therefore the modified method of Cheng et al, 1987 could be used to extract DNA from rice varieties instead of the commercially available expensive and hazardous DNeasy plant kit method supplied by QIAGEN.Int J Appl Sci Biotechnol, Vol 3(3): 398-401
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".