Honoring the Complexity of Genetics: Exploring the Role of Genes and the Environment Using Real World Examples
Bibliographic record
Abstract
Historically, undergraduate genetics courses have disproportionately focused on the impact of genes on phenotypes, rather than multifactorial concepts which consider how a combination of genes, the environment, and gene-by-environment interactions impacts traits. Updating the curriculum to include multifactorial concepts is important to align course materials to current understanding of genetics, and potentially reduce deterministic thinking, which is the belief that traits are solely controlled by genes. Currently there are few resources to help undergraduate biology instructors incorporate multifactorial concepts into their genetics courses, so we designed this lesson that centers on familiar, real-world examples. During this lesson, students learn how to distinguish between genetic and environmental sources of variation, and examine and interpret examples of how phenotypic variation can result from a combination of gene and environmental variation and interactions. This lesson, which is designed for both in-person and online classrooms, engages students in small group and large group discussion, figure interpretation, and provides questions that can be used for both formative and summative assessments. Results from assessment questions suggest that students found working through models depicting the interactions between genotypes and environments beneficial for their understanding of these complex topics. Primary Image: Mendel’s laws of alternative inheritance of peas. A photo taken by W.F.R. Weldon of variation in color and texture of peas. Reprinted with permission from Biometrika (Weldon WFR. 1902. Mendel’s laws of alternative inheritance in peas).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".