Bibliographic record
Abstract
Although rubber science and technology is approximately two centuries old, the greatest growth in the industry occurred in the 20th century, through a strong association with the pneumatic tire and the automotive industry.In 2000, overall rubber consumption was around 18 million tons, with natural rubber accounting for some 7250 ktons.*In Europe the rubber industry is very important, with a production of more than 3.2 million tons per year-around 61% for tire manufacturing and the rest for so-called industrial rubber goods.† About a quarter million persons are employed by manufacturers of tires and rubber goods.Such important industrial activity is supported by research and development activities, not only in companies but also in a number of university research laboratories, as reflected by the steady flow of publications dealing with rubber science and technology.The International Seminars on Elastomers have resulted from the initiative of individuals, most of them belonging to institutions or universities, and are becoming quite a tradition.The first seminar was organized in 1977 by Prof. M. Morton, at the time Director of the Institute of Polymer Science at the University of Akron, and Prof. N. Yamakzaki.Since that time, several seminars have been organized in various placesthe United States, Japan, South Korea, and Thailand-by a number of scientists engaged in rubber research (see Table I).Papers from the 3rd, 4th, and 5th seminars were published in the Journal of Applied Polymer Science, Applied Polymer Symposia, Vol.44 (1989), Vol.50 (1992), and Vol.53 (1994), respectively.A detailed historical account of those seminars was provided by Dr. K. Suchiva, Prof. J. L. White, and Prof. Y. Tanaka in the preface to a special issue of the Journal of Applied Polymer Science (Vol.78, No. 8, 2000) including contributions selected from the 7th International Seminar on Elastomers, held in Bangkok, Thailand, in December 1998.Following the success of the 7th seminar, it was decided that the next seminar would be held, for the first time, in Europe.A team of university professors and their collaborators agreed to organize the event in France.The city of Le Mans was considered an obvious site for the seminar, as significant quantities of tire tread are worn there every year in a well-known Grand Prix.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.459 | 0.306 |
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".