Recent Advances in the Preparation of Allylboronates and Their Use in Tandem Reactions with Carbonyl Compounds
Bibliographic record
Abstract
This chapter contains sections titled: Introduction Preparation of Allylboronates Direct Methods Allylboronates from Allylmetal Intermediates Allylboronates from Alkenylmetal Intermediates Allylboronates from the Hydroboration of 1,3-Butadienes and Allenes Allylboronates from the Transition-metal Catalyzed Diboration and Silaboration of Dienes and Allenes Allylboronates from Palladium-catalyzed Cross-coupling Reactions with Alkenyl Fragments Allylboronates from Palladium-catalyzed Cross-coupling Reactions with Allyl Electrophiles Indirect Methods Allylboronates from Alcoholysis of Triallylboranes Allylboronates from Homologation of Alkenylboronates Allylboronates from Allylic Rearrangement of Alkenylboronates Allylboronates from Isomerization of Alkenylboronates Allylboronates by Cycloadditions of Dienylboronates Allylboronates by Olefin Metathesis Reactions of Allylboronates Additions to Aldehydes – Formation of Homoallylic Alcohols Stereoselectivity and Mechanism of Non-catalyzed Additions Lewis Acid-catalyzed Additions Stereoselective Additions with Chiral Allylboronates Additions to Ketones Additions to Imine Derivatives Applications of Allylboronates in Tandem Reactions with Carbonyl Compounds Allylboration as the Terminal Process Tandem [4+2] Cycloaddition/Allylation Tandem Hydroformylation/Intramolecular Allylation Tandem Alkene Cross-metathesis/Allylation Tandem Diene Hydroboration/Allylation Tandem Diene Diborylation (Silaboration)/Allylboration Tandem Allylic Borylation/Intramolecular Allylation Allylboration as the Initiating Process Tandem Allylation/Allylation Tandem Allylation/Lactonization Tandem Allylation/Dioxene Thermolysis Conclusion References
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".