Bibliographic record
Abstract
Throughout undergraduate curriculum in North America, the requirement for understanding structure is explicit. For example, in first year general chemistry, Valence Shell Electron Pair Repulsion (VSEPR) theory is used to predict the geometric shape of a molecule based on its electron repulsion forces. The shape of the molecule is determined by minimizing (bonding and non-bonding) electron group repulsions surrounding the central atom(s). [1] Methodology to achieve understanding of concepts related to structure, however, is ordinarily not explicit, and in the case of VSEPR, student learning activities have traditionally involved modeling molecular shapes using manufactured kits, or with materials that are readily available (for example, Styrofoam balls, or marshmallows and toothpicks.) [2] These activities are based on ideal geometry assumptions, and lead to the question, for molecules with a combination of bonding and non-bonding electron groups, how much is "less than" ideal angles? Similarly, in senior chemistry courses, students face structural questions related to resonance, coordination number, the explanation of spectroscopic features, and magnetic properties. The use of active learning engagement through structural database explorations can be employed to address questions at both firstyear, and senior levels. Practical approaches to these questions will be explored, with an emphasis on using information available from the Cambridge Structural Database. [3]
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".