Bibliographic record
Abstract
Welcome to the Journal of Biocommunication’s Special Issue 45-1. We have designated this publication as a JBC “Special Issue,” as it is devoted entirely to one topic. Our current Special Issue includes articles and commentaries all related to Eduard Pernkopf’s, Atlas of Topographical and Applied Human Anatomy. Our authors have provided in-depth discussions about the Pernkopf’s atlas’ dark history, the uncertain origin of cadavers used as references for the atlas, and medical crimes of the Third Reich. Seven of the articles are authored by some of the world’s leading historians and authorities on the subject of the Pernkopf atlas and the abuses of Nazi medicine. These authors presented papers at a Holocaust Education Week Symposium that was held on Nov. 10, 2019, at the Temerty Faculty of Medicine, University of Toronto, Toronto, Canada. This landmark Symposium was called, “The Vienna Protocol: Medicine’s Confrontation with Continuing Legacies of its Nazi Past.” The Symposium faculty included Susan Mackinnon, MD, Rabbi Joseph Polak, William E. Seidelman, MD, Sabine Hildebrandt, MD, Philip Berger, MD, Anne Agur, PhD, and Leila Lax, PhD, who also served as the Symposium coordinator and host. Table of Contents image credit: Medical University of Vienna, MUW-AD-003250-5-ABB-81.
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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 | 0.012 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.113 | 0.065 |
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".