A Problem-Based Introduction to Machine Learning in the Undergraduate Organic Chemistry Laboratory: Prediction of Diels–Alder Reaction Rates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To engage students in higher-order thinking skills, an inquiry-based dry laboratory experience was developed for upper-year undergraduate students, where students were introduced to machine learning approaches to solve chemical problems. Students constructed their own data set of diene and dienophile features and performed a multivariate linear regression in a Python environment to predict the energy barriers (Δ G ‡ ) of a Diels–Alder system. They applied their models to a simulated drug development problem. Likert-scale surveys and qualitative interviews were utilized to collect data on student experiences. Students expressed that they felt strongly engaged in critical and creative thinking, collaboration, and metacognition. Subsequently, students felt that computational tools were more approachable, and had a stronger appreciation of how computational tools could be utilized in chemistry contexts. Students also expressed that they felt the freedom to make mistakes, reflect, and improve, embracing a growth mindset in this laboratory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it