Polymerization Processes, 2. Modeling of Processes and Reactors
Bibliographic record
Abstract
The article contains sections titled: 1. Introduction 2. Fundamental Effects of Reactor Types 2.1. Reactor Types and Their Models 2.2. General Effects on the Molecular Mass Distribution (MMD) 3. Processes and Reactor Modeling for Step Polymerization 3.1. Types of Reactors and Reactor Modeling 3.2. Specific Processes 3.2.1. Polyamides 3.2.2. Polyesters 4. Processes and Reactor Modeling for Chain Polymerization 4.1. Introduction to Polymerization Techniques 4.2. Fundamentals of Material Balance Equations 4.3. Effect of Reactor Types on Copolymer Composition Distribution 4.4. Effect of Reactor Types on Nonlinear Polymer Formation 5. Bulk and Solution Polymerization 5.1. Removal of Solvent and Residual Monomer 5.2. Systems with Polymer‐Polymer Demixing 6. Precipitation and Dispersion Polymerization 6.1. Polymerization without Solvent 6.2. Polymerization with Solvent 7. Suspension Polymerization 7.1. Process Description 7.2. Polymerization Kinetics 8. Emulsion Polymerization 8.1. Process Description 8.2. Polymerization Kinetics 8.3. Molecular Mass Distribution 8.3.1. Linear Polymerization 8.3.2. Nonlinear Polymerization 8.4. Effect of Small Reaction Loci on Reversible‐‐Deactivation Radical Polymerization 8.4.1. Stable Radical‐Mediated Polymerization (SRMP) and Atom‐Transfer Radical Polymerization (ATRP) 8.4.2. Reversible Addition–Fragmentation Chain Transfer (RAFT) Polymerization
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".