Bibliographic record
Abstract
This chapter contains sections titled: Introduction Kinetics and Mechanism Autoxidation Inhibition by Phenols Antioxidant Activity and Stoichiometric Factor—H-atom Transfer and Electron Transfer Mechanisms Reaction Products of Antioxidants: α-Toc Efficiencies of Phenolic Antioxidants Some Experimental Methods Reaction of Phenolic Antioxidants with Peroxyl Radicals Inhibited Oxygen Uptake (IOU) Measurement Techniques Product Studies—Hydroperoxide Products Reaction of Phenolic Antioxidants with Other Radical Sites Overall Assessment of Strategies to Determine Antioxidant Activities Structural Effects on Efficiencies of Antioxidants Monohydroxy Phenols: Substituent Effects Dihydroxy Phenols: Catechols and 1,4-hydroquinones—Intramolecular Hydrogen Bonding Revisited Media Effects Solvent Effects Solvent Interactions with the Attacking Radicals Solvent Interactions with Phenolic Antioxidants—Effects on Antioxidant Mechanisms Antioxidants in Heterogeneous Systems: Lipid Peroxidation and Inhibition in Micelles and Lipid Membranes Monohydroxy Phenols. Factors Controlling Antioxidant Activities in Membranes. The Unique Behavior of α-Toc Di- and Polyhydroxy Phenols in Membranes. Ubiquinols and Flavonoids Chemical Calculations on Phenols Introduction Application to Antioxidants Antioxidant Mechanisms by Phenols: Hydrogen Atom Transfer (HAT) and Single Electron Transfer (SET) Calculations of Substituent Effects for Monophenols Calculations of More Complex Polyhydroxy Phenols Future Prospects for Antioxidants References
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) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".