Workshop Report: Summer 2020 Virtual CRISPR in the Classroom
Bibliographic record
Abstract
As innovations and developments in genome editing technologies using CRISPR-Cas systems progress, the need to disseminate relevant knowledge and build skills among the next generation of young scientists in undergraduate classrooms is vital. Our efforts to enable undergraduate educators to bring CRISPR into their classrooms through in-person workshop training began in 2017 and went virtual during summer of 2020 under COVID-19 lockdown. In this report, we describe the proceedings of the virtual workshop and the feedback we received from the participants. An overwhelming majority of attendees reported that the virtual workshop facilitated gains in learning about CRISPR biology and experimental design. The plans shared by attendees to incorporate both virtual and hands-on CRISPR resources into their courses highlights the impact of this virtual CRISPR in the Classroom Workshop on educator confidence, and the likelihood of attendees to add CRISPR biology to their curriculum after participating in such a workshop. Primary image: A summary diagram of a virtual workshop on bringing CRISPR biology into the classroom. The molecular rendering created with Illustrate software features Cas9 (gray) in complex with guide RNA (red) and target DNA (blue) (PDB ID: 4OO8, (1, 2)).
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.164 | 0.066 |
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".