Some comments on the life and publications of Jerzy K. Baksalary (1944-2005)
Bibliographic record
Abstract
Following some biographical information on Jerzy K. Baksalary (1944–2005) and some comments by Tadeusz Calinski, Oskar Maria Baksalary, and Image Editors-in-Chief: Bryan L. Shader and Hans Joachim Werner, this article continues with personal remarks on the life and publications of Jerzy K. Baksalary by R. William Farebrother, Jurgen Gros, Jan Hauke, Radoslaw Kala, Erkki Liski, Xiaoji Liu, Augustyn Markiewicz, Wieslaw Migdalek, Friedrich Pukelsheim, Tarmo Pukkila, Simo Puntanen, C. Radhakrishna Rao, George P.H. Styan, Tomasz Szulc, Yongge Tian, Gotz Trenkler, Julia Volaufova, Haruo Yanai, and Fuzhen Zhang. These remarks are followed by a detailed list of, and some comments on, Jerzy Baksalary’s publications prepared by the editors of this article. Four photographs of Jerzy Baksalary illustrate the article, with three of these also including some of his coauthors, colleagues, and Ph.D. students.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".