Bibliographic record
Abstract
Samuel Kotz was born in Harbin, China, on August 28, 1930. After graduating with honors in 1946 from the Russian School in Harbin, he studied electrical engineering at Harbin Institute of Technology during 1947-1949. In 1949 he emigrated to Israel, where, after two years of military service, he studied at the Hebrew University in Jerusalem, obtaining an M.A. with honors in Mathematics in 1956. Following two years at the Israeli Meteorological Service, he entered graduate school at Cornell University and obtained a Ph.D. degree in Mathematics in 1960. After research positions at the University of North Carolina, Chapel Hill, and the University of Toronto, he joined the latter institution as an Associate Professor in 1964. He moved to Temple University, Philadelphia, in 1967 as Professor of Mathematics and then to the University of Maryland, College Park, in 1979 as Professor in the College of Business and Management. He took early retirement and moved to George Washington University in 1997. Samuel Kotz has made substantial contributions in several areas of statistics--including systems of distributions, measures of dependence, multivariate analysis, characterizations, limit distributions, replacement theory, quality control, information theory and applications of statistics. He is the senior coeditor-in-chief of the thirteen-volume Encyclopedia of Statistical Sciences, an author or coauthor of over one hundred and fifty articles on statistical methodology and theory, twelve books in the field of statistics and quality control and three Russian-English scientific dictionaries and coauthor of the often-cited compendium of statistical distributions. His efforts, excellence and contributions were recognized by the award of honorary Doctor of Science degrees from Harbin Institute of Technology (China) in 1988, from the University of Athens (Greece) in 1995 and from Bowling Green State University (Ohio, U.S.A.) in 1997. In 1997 a volume containing thirty-eight essays was published in honor of his sixty-fifth birthday. He was awarded membership in the Washington Academy of Sciences in 1998. He is a Fellow of the Royal Statistical Society, Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".