An Interactive Protocol for In-Classroom DNA Extraction
Bibliographic record
Abstract
Using commonly available materials, this tool allows students to extract DNA, exploring DNA chemistry and the principles of experimental design and execution. We take a “Choose Your Own Adventure” approach encouraging students to explore the protocol and vary individual steps. Students learn the science behind each step of extraction, how that science can allow us to identify and understand certain aspects of the structure of DNA, and how modifying experimental steps can change the observed results. The lesson is intended for an undergraduate setting, but we include adaptations to allow delivery of this lesson to a variety of ages from preschool through adult science events. The manuscript is in English, but we have included supporting materials in Anishinaabemowin, French, Spanish, Urdu, Arabic, Japanese, Mandarin, Hindi, Twi, and English, so that more learners can access these materials in their first language. We have included a supplemental figure showing the simplified structure of DNA using a color scheme that is effective with those with typical sight and colorblindness. We have also linked a video demonstration of the extraction that is available in both French and English and with closed captioning. Inclusion of materials in multiple languages and formats makes the material more user-friendly, allowing its direct inclusion in non-English speaking classrooms, and allows learners to understand that science is not limited to the “universal” scientific language and can be conducted in any language of choice. Primary Image: This image highlights the basic steps of the extraction process, showing the experimental setup, the DNA precipitation, the product and variation observed amongst different group members.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.403 | 0.146 |
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".