Non Omnis Moriar<br>In memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014)
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Iżycka-Świeszewska E, Kulig A, Balcerska A. Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014). Polish Journal of Pathology. 2015;66(2):101-102. doi:10.5114/pjp.2015.52998. APA Iżycka-Świeszewska, E., Kulig, A., & Balcerska, A. (2015). Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014). Polish Journal of Pathology, 66(2), 101-102. https://doi.org/10.5114/pjp.2015.52998 Chicago Iżycka-Świeszewska, Ewa, Andrzej Kulig, and Anna Balcerska. 2015. "Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014)". Polish Journal of Pathology 66 (2): 101-102. doi:10.5114/pjp.2015.52998. Harvard Iżycka-Świeszewska, E., Kulig, A., and Balcerska, A. (2015). Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014). Polish Journal of Pathology, 66(2), pp.101-102. https://doi.org/10.5114/pjp.2015.52998 MLA Iżycka-Świeszewska, Ewa et al. "Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014)." Polish Journal of Pathology, vol. 66, no. 2, 2015, pp. 101-102. doi:10.5114/pjp.2015.52998. Vancouver Iżycka-Świeszewska E, Kulig A, Balcerska A. Non Omnis MoriarIn memoriam Professor Andrzej Roszkiewicz (27.05.1941 – 12.09.2014). Polish Journal of Pathology. 2015;66(2):101-102. doi:10.5114/pjp.2015.52998.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.418 | 0.377 |
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".