Enhancing Undergraduate Teaching and Research with a<i>Drosophila</i>Virginizing System
Bibliographic record
Abstract
Laboratory exercises using Drosophila crosses are an effective pedagogical method to complement traditional lecture and textbook presentations of genetics. Undergraduate thesis research is another common setting for using Drosophila. A significant barrier to using Drosophila for undergraduate teaching or research is the time and skill required to accurately collect virgins for use in controlled crosses. Erroneously collecting males or nonvirgin females contaminates crosses with unintended genotypes and confounds the results. Collecting adequate numbers of virgins requires large amounts of time, even for those skilled in virgin collection. I have adapted an effective method for virgin collection that eliminates these concerns and is straightforward to use in undergraduate settings. Using a heat-shock-induced, conditional lethal transgene specifically in males, male larvae can be eliminated from a culture before adults eclose. Females thus eclose in the absence of males and remain virgin, eliminating the need to laboriously score and segregate freshly eclosed females. This method is reliable, easily adaptable to any desired phenotypic marker, and readily scaleable to provide sufficient virgins for large laboratory classes or undergraduate research projects. In addition, it allows instructors lacking Drosophila expertise to use this organism as a pedagogical tool.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".