Box of Lessons: An Open Educational Resource for Exploring Biomolecular Structure and Function
Bibliographic record
Abstract
Structure-function relationships are a core concept in many STEM disciplines. Most biology curricula introduce students to macromolecules, their building blocks, and other small molecules that play key roles in biological processes. However, the shapes, interactions, and functions of these molecules are often discussed using schematic diagrams, ignoring the vast amounts of three-dimensional structural and bioinformatics data freely available from public data resources. Keeping up with and incorporating the rapidly evolving data, tools, and resources in suitable, structure-function focused lessons can be time-consuming and challenging. A group of experienced biology, chemistry, and biochemistry educators collaboratively developed the "Box of Lessons" (BOL) to engage students and educators in authentic explorations, reinforcing disciplinary concepts, while developing skills in biomolecular visualization and use of public bioinformatics data resources and tools. The BOL consists of multimedia learning materials, ready-to-use, student- and educator-facing worksheets with teaching notes, aligned with ASBMB learning goals. Materials in this collection have been reviewed and piloted by undergraduate educators before its publication on PDB-101, as modular open educational resources. Educators are encouraged to select BOL elements relevant to their curricular context and adapt them to fit their students' needs and learning goals.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.029 |
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".