Proportions, not numbers - a computer simulation that facilitates students' understanding ofnatural selection
Bibliographic record
Abstract
Students hold several misconceptions related to natural selection and evolution. They therefore often find it difficult to explain mechanisms and predict when evolution will occur. For example, if a population of white rabbits is preyed upon by wolves, students may state that evolution has occurred because the number of rabbits decreased (even though the proportion of white rabbits stayed the same). Alternatively, students may state that evolution will occur because rabbits are forced to change fur color. This activity gives students an opportunity to confront their misconceptions. Students first predict the outcome of different scenarios, e.g. in the presence or absence of variation within a population and in the presence or absence of selection pressures. Thereafter, they run a simulation and graph changes in the number as well as the proportion of individuals with different traits over time to test their hypotheses. The activity can be expanded upon in several ways and is suitable for introductory biology for both majors and non-majors. The exercise can be used in lab, in lecture, or be assigned as an assignment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".