Greenbeards in yeast: an undergraduate laboratory exercise to teach the genetics of cooperation
Bibliographic record
Abstract
Recent years have seen a dramatic increase in our understanding of the social behaviour of microbes. Here, we take advantage of these developments to present an undergraduate laboratory exercise that uses the cooperative flocculating behaviour of yeast (Saccharomyces sp.) to introduce the concept of inclusive fitness and teach the genetics of cooperation. Students generate their own data using co-cultures of various yeast strains and perform statistical analyses to test whether kin selection or greenbeard effects determine the cooperative flocculating behaviour. The lab has run successfully for two consecutive years in a second year course with some 1, 200 students per year at the University of Toronto, Canada. We discuss the benefits of using microbes to teach social evolution, describe the set-up and learning outcomes of the laboratory exercise, and then outline possible extension and variants of the lab. In addition to providing students with the opportunity to use a model organism to study social behaviour, students are also taught common laboratory skills, such as replica plating and sterile techniques. Ultimately, while the genetics of cooperation has traditionally been taught through computer simulations and evolutionary games, this exercise demonstrates a way to experimentally introduce the topic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".