Comparison of Both Preparation Methods of PCR Templates of Genomic DNA from Actinobaceteria
Bibliographic record
Abstract
[Objective]The aim was to compare the two preparation methods of PCR templates of genomic Dna from Actinobaceteria. [Method]16 strains of 8 genera separated and identified from saline soil of Langfang,Hexi,including Saccharothrix Ⅳ23-3-4,NocardioidesⅤ22-5-1,Promicromonospora Ⅸ5-4-3,Micromonospora Ⅱ23-4-1,Actinopolyspora Ⅳ8-2-5,Nocardiopsis Ⅱ8-4-3,Streptomonospora DA01305,Streptomyces golden groups DA03401,Streptomyces golden groups DA01408,Streptomyces blue groups DA09140,Streptomyces golden groups DA01308,Streptomyces ash,red and purple groups DA05213,Streptomyces blue groups DA11417,Streptomyces pink spore groups DA04401,Streptomyces ash,red and purple groups DA04402,Streptomyces ball spore groups DA04307。The liquid nitrogen grinding method and the TE boiling method was used to treat Actinobaceteria in order to explore the rapid preparation method of PCR template,and the PCR template was amplified of 16S rDNA,then PCR product was detected by electrophoresis. [Method]The results showed the two methods both got the clear objective band,while the TE boiling method simplified the PCR template preparation process of DNA from Actinobaceteria,so could be used to prepare the PCR template of high-throughput Actinobaceteria rapidly. [Conclusion] The study provides an effective method for repaid identification and systematic classification of large quantities of strains.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".