Synthetic Rubbers, Producers and World Market of
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.000 |
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 it