<scp>DNA</scp> barcoding: A different perspective to introducing undergraduate students to <scp>DNA</scp> sequence analysis
Bibliographic record
Abstract
Education in biochemistry teaching laboratories focus primarily on applying biochemical techniques to understanding human disease, biochemistry, and biotechnology. With anthropogenic climate change, there is a renewed interest in quantifying biodiversity, especially with the use of molecular-based approaches such as DNA barcoding. This 3-week laboratory exercise allowed undergraduate students to explore DNA sequencing, analysis, and DNA barcoding. Students extracted DNA from insect legs and amplified a 650 bp section of Cytochrome C oxidase I gene by PCR, and confirmed the success of their PCR by DNA gel electrophoresis. The PCR products were submitted for sequencing and students analyzed the sequences using FinchTV, Genbank, and the Barcode of Life Database. Based on the DNA sequences of their PCR products students were able to identify the species of insects. This lab exercise provides a different context to introducing students to analyzing DNA sequences and using DNA databases.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".