A Brief History of Prohibition and Treatment Solutions for Substance Abusers
Bibliographic record
Abstract
Background: Molecular characteristics are good markers for distinguishing between closely related species. Molecular taxonomy has been significantly improved by DNA-based strategies with increasing accuracy and efficiency in identifying species through the use of PCR-based methods. We used inter-simple sequence repeat (ISSR) markers in this research to compare and examine the genetic profiles of several Oscillatoria species from native freshwater environments.Methods: Algal samples were taken from natural freshwater environments in the autumn season (September–November 2022). To reduce contamination, DNA was obtained from unialgal cultures of Oscillatoria spp. Then, ISSR analysis was done to assess genetic diversity and phylogenetic relationships between the isolated strains.Results: Seven species were identified: Oscillatoria acuta (OS1), O. princeps Vaucher (OS2), O. annae (OS3), O. margaritifera (OS4), O. proteus Skuja (OS5), Oscillatoria sp. (OS6), and O. sancta (OS7). O. annae and Oscillatoria sp. were most similar and were found to have the greatest genetic similarity (index = 0.6598), indicating that a close evolutionary relationship exists. The two with the lowest similarity were O. acuta and O. proteus Skuja (index = 0.4330), indicating greater genetic divergence.Conclusion: Our results support the use of ISSR markers in determining genetic diversity and phylogenetic relationships among Oscillatoria spp. This method is promising for improving the molecular taxonomy of cyanobacteria.Keywords: Genetic Diversity, Oscillatoria, Phylogenetic analysis, Cyanobacterial taxonomy, ISSR markers
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.019 |
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".