The Use of a Small Chalcone Spectroscopy Database for the Introduction of Advanced Spectroscopy Techniques at the Undergraduate Levels
Bibliographic record
Abstract
Teaching organic spectroscopy and structure elucidation at the undergraduate level (IR, UV, NMR spectroscopy and mass spectrometry) often requires the use of ideal targets and flawless spectra, which can mislead students about the use of spectroscopy to solve every given structure. It is common for undergraduate students to use NMR spectroscopy to solve their unknown structures while discarding simpler analytical tools or methods. Substituted chalcones proved to be the perfect targets to teach students that NMR spectroscopy may not always be the definitive tool to analyze and identify a structure and that the knowledge of the uses and limitations of other spectroscopic methods is critical when trying to solve a problem. In this article, we have synthesized a series of mono- and disubstituted chalcones to be used as teaching examples for undergraduate students. This article teaches unequivocal structure identification and spectra assignment using IR, UV, NMR “full packages” and mass spectrometry, while supplying students with a large database of practice questions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.034 |
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".