A predictive tool for assessing <sup>13</sup>C NMR chemical shifts of flavonoids
Bibliographic record
Abstract
Herein are presented the (1)H and (13)C NMR data for seven monohydroxyflavones (3-, 5-, 6-, 7-, 2'-, 3'-, and 4'-hydroxyflavone), five dihydroxyflavones (3,2'-, 3,3'-, 3,4'-, 3,6-, 2',3'-dihydroxyflavone), a trihydroxyflavone (apigenin; 5,7,4'-trihydroxyflavone), a tetrahydroxyflavone (luteolin; 5,7,3',4'-tetrahydroxyflavone), and three glycosylated hydroxyflavones (orientin; luteolin-6C-beta-D-glucoside, homoorientin; luteolin-8C-beta-D-glucoside, vitexin; apigenin-8C-beta-D-glucoside). When these NMR spectra are compared, it is possible to assess the impact of flavone modification and to elucidate detailed structural and electronic information for these flavonoids. A simple predictive tool for assigning flavonoid (13)C chemical shifts, which is based on the cumulative differences between the monohydroxyflavones and flavone (13)C chemical shifts, is demonstrated. The tool can be used to accurately predict (13)C flavonoid chemical shifts and it is expected to be useful for rapid assessment of flavonoid (13)C NMR spectra and for assigning substitution patterns in newly isolated flavonoids.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".