Professor Ivan Ivanovich Neiding: Touches to the portrait
Bibliographic record
Abstract
2024 marks the 120th anniversary of the death of Ivan Ivanovich Neiding, a prominent Russian forensic scientist and professor of the medical faculty of Imperial Moscow University. The formative years of forensic medicine in Russia are inextricably linked to natural development and success in medical science. An analysis of the history of the forensic medical service also showed its close connection with the law and the needs of judicial investigative institutions. This interaction revealed the nature of scientific research and the practical orientation of forensic medical examination. I.I. Neiding has made significant contributions to the development of Russian forensic medical science and practice and was one of the best representatives of the faculty of Moscow University at the end of the XIX century. His name is associated with the period of brilliant scientific flourishing of the Department of Forensic Medicine of the Faculty of Medicine. During his 22 years as head of the department, I.I. Neiding had done a lot to ensure that the teaching of forensic medicine meets the ever-increasing practical demands. The creative path of this scientist can be an example for young specialists in the field of forensic medicine. His image was recreated based on the memoirs of his contemporaries and speeches. The article provides information about the life and work of I.I. Neiding. The memoirs of this scientist’s contemporaries and views on forensic medicine are presented. This study has not only cognitive significance but also carries the idea of educating young professionals. Among others, I.I. Neiding makes up the color of Russian medicine and pedagogy. Many of the provisions of I.I. Neiding’s research have not lost their relevance even today.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".