A molecular genetic lab to generate inclusive and exclusive forensic evidence: Two suspects, a victim, and a bloodstained T‐shirt
Bibliographic record
Abstract
Molecular genetic laboratory exercises can be ineffective due the student's lack of connection to the complex and sequential protocols. In this inquiry-based molecular genetic laboratory exercise, we harness students' fascination with human forensics and provide a real-life scenario using biomolecular techniques to identify "whose blood is on the t-shirt." We use fish blood to create realistic blood stains on clothing and challenge the students to use DNA analyses to clear or implicate suspects. Safety concerns are minimized through the use of fish blood, while maximizing both realism and the likelihood of student success due to fishes' nucleated red blood cells. The goal in designing this laboratory exercise was to create a feasible protocol for large (over 300 students) second year university courses. During two 3 hour laboratory sessions, students learn and apply clean/sterile technique, DNA extraction, polymerase chain reaction, restriction fragment length polymorphisms, and agarose gel electrophoresis. The students also learn to interpret the resulting gel bands in terms of inclusive or exclusive evidence. Students have consistently ranked this lab as their favorite of five taken as part of a second year Genetics course.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".