Metagenomic Approaches to Identify Novel Organisms from the Soil Environment in a Classroom Setting
Bibliographic record
Abstract
Molecular Microbial Metagenomics is a research-based undergraduate course developed at Georgia State University. This semester-long course provides hands-on research experience in the area of microbial diversity and introduces molecular approaches to study diversity. Students are part of an ongoing research project that uses metagenomic approaches to isolate clones containing 16S ribosomal ribonucleic acid (rRNA) genes from a soil metagenomic library. These approaches not only provide a measure of microbial diversity in the sample but may also allow discovery of novel organisms. Metagenomic approaches differ from the traditional culturing methods in that they use molecular analysis of community deoxyribonucleic acid (DNA) instead of culturing individual organisms. Groups of students select a batch of 100 clones from a metagenomic library. Using universal primers to amplify 16S rRNA genes from the pool of DNA isolated from 100 clones, and a stepwise process of elimination, each group isolates individual clones containing 16S rRNA genes within their batch of 100 clones. The amplified 16S rRNA genes are sequenced and analyzed using bioinformatics tools to determine whether the rRNA gene belongs to a novel organism. This course provides avenues for active learning and enhances students' conceptual understanding of microbial diversity. Average scores on six assessment methods used during field testing indicated that success in achieving different learning objectives varied between 84% and 95%, with 65% of the students demonstrating complete grasp of the project based on the end-of-project lab report. The authentic research experience obtained in this course is also expected to result in more undergraduates choosing research-based graduate programs or careers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".