Additional file 1 of Synthesis, biological evaluation, and computational studies of some novel quinazoline derivatives as anticancer agents
Bibliographic record
Abstract
Additional file 1: Figure S1. The FT-IR spectrum of 7a. Figure S2. The 1H NMR spectrum of 7a. Figure S3. The 13C-NMR spectrum of 7a. Figure S4. The Mass spectrum of 7a. Figure S5. The FT-IR spectrum of 7b. Figure S6. The 1H NMR spectrum of 7b. Figure S7. The 13C-NMR spectrum of 7b. Figure S8. The Mass spectrum of 7b. Figure S9. The FT-IR spectrum of 7c. Figure S10. The 1H NMR spectrum of 7c. Figure S11. The 13C-NMR spectrum of 7c. Figure S12. The Mass spectrum of 7c. Figure S13. The FT-IR spectrum of 7d. Figure S14. The 1H NMR spectrum of 7d. Figure S15. The 13C-NMR spectrum of 7d. Figure S16. The Mass spectrum of 7d. Figure S17. The FT-IR spectrum of 7e. Figure S18. The 1H NMR spectrum of 7e. Figure S19. The 13C-NMR spectrum of 7e. Figure S20. The Mass spectrum of 7e. Figure S21. The FT-IR spectrum of 7f. Figure S22. The 1H NMR spectrum of 7f. Figure S23. The 13C-NMR spectrum of 7f. Figure S24. The Mass spectrum of 7f. Figure S25. The FT-IR spectrum of 7g. Figure S26. The 1H NMR spectrum of 7g. Figure S27. The 13C-NMR spectrum of 7g. Figure S28. The Mass spectrum of 7g. Figure S29. The FT-IR spectrum of 7h. Figure S30. The 1H NMR spectrum of 7h. Figure S31. The 13C-NMR spectrum of 7h. Figure S32. The Mass spectrum of 7h.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.859 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".