Bibliographic record
Abstract
This article is an account of our efforts over the last decade to functionalize phenols and anilines at any position and to use these compounds to generate substituted aromatic systems and advanced unsaturated cyclohexanone moieties, enabling the rapid formation of complex structures. Total syntheses of numerous natural products involving such intermediates were achieved. 1 Introduction 2 ortho-Functionalization of Phenols and Aniline Derivatives Mediated by Iodanes (III) and Synthesis of Panacene 2.1 Cross-Coupling with Aniline Derivatives 2.2 Dearomative Cycloaddition of Arenes and Heteroarenes 2.3 Total Synthesis of Panacene 3 meta-Functionalization of Aniline Derivatives and Synthesis of Erysotramidine 3.1 meta-Functionalization of Aniline Derivatives 3.2 Total Synthesis of Erysotramidine 4 para-Functionalization of Phenols and Applications in Total Synthesis 4.1 Bimolecular Approach Mediated by Protecting Groups 4.2 ipso-Rearrangement 4.3 Oxidative Alkyl Shift 4.4 Oxidative Prins-Pinacol Rearrangement 4.5 Oxidative Prins-Type Reaction 4.6 Total Synthesis of (–)-Fortucine 4.7 Total Synthesis of Isostrychnine 4.8 Total Synthesis of (–)-Strychnopivotine 5 Development of a Functional Protecting Group
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 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 | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".