Synthetic Rubbers, Producers and World Market of
Bibliographic record
Abstract
Introduction Market and Areas of Application Nomenclature and Classification Properties Production Producers Emulsion Styrene–Butadiene Rubber (E-SBR) Producers and Production Capacities Chloroprene Rubber (CR) Producers and Production Capacities Nitrile Rubber (NBR) Producers and Production Capacities Emulsion Polybutadiene (E-BR) Producers and Production Capacities Acrylate Rubber (ACM) Producers and Production Capacities Fluororubbers (Logothetis, 1989, 1992; Cook and Lynn, 1990) Fluororubbers Producers, Production, Capacities, and Markets Synthesis by Anionic Polymerization Producers and Production Capacities (The Synthetic Rubber Manual, 1989; IISRP, 1991) Producers and Production Capacities Synthesis by Ziegler–Natta Polymerization Producers and Production Capacities Synthesis of Butyl Rubber by Cationic Polymerization (Kirk-Othmer, 1991–1998; Kresge et al., 1987) Synthesis of Butyl Rubber by Cationic Polymerization Producers and Production Capacities EVM and Ethylene Copolymers Producers and Production Capacities Epoxide Rubbers (CO, ECO, GECO, GPO) Producers and Production Capacities Polynorbornene Uses Economic Aspects Polyoctenamers Economic Aspects Silicone Rubber Producers and Markets Thiokol Rubber Producers and Production Capacities Halobutyl Rubber Producers and Production Capacities Chloropolyethylene and Chlorosulfonyl Polyethylene Producers and Production Capacities Hydrogenated Nitrile Rubber Producers and Production Capacities Polyphosphazenes Producers and Markets Evaluation of the Present Situation and Remarks on Future Trends Market Producers Tire-Manufacturing Industry Manufacturers of Technical Rubber Goods Rubber Toughening of Thermoplastic and Thermoset Materials
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".