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Record W4393209406 · doi:10.21638/spbu11.2023.308

The path to science

2023· article· en· W4393209406 on OpenAlexaboutno aff
Alexander Zubritsky

Bibliographic record

VenueVestnik of Saint Petersburg University Medicine · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPath (computing)Computer sciencePsychologyComputer network

Abstract

fetched live from OpenAlex

Alexander Nickolaevich Zubritsky is a pathologist of the highest category, a member of the European Society of Pathology, Professor. He was born on March 14, 1949 in Severo-Kurilsk, Sakhalin Region in the family of a military therapist. At age 15, worked as a hospital attendant in a pathology department of a hospital in Sverdlovsk and studied at night school for working youth. In 1974, he graduated the curative and preventive faculty of the Sverdlovsk Medical Institute. In 1974–1975, was an internship in pathological anatomy at the basis of Sverdlovsk Regional Clinical Hospital. In 1977, enrolled in correspondence postgraduate study at the Institute of Human Morphology in Moscow on a specialty “pathological anatomy” under the direction of the Hero of Socialist Labor, Lenin Prize laureate, Academician of AMS USSR Prof. A. I. Strukov. In 1990, he defended his thesis “Quantitative analysis of pathomorphological changes in the right ventricle of the heart in group of patients with chronic nonspecificpulmonary diseases” in the I. M. Sechenov Moscow Medical Academy. In 1990, he became a finalist of the Marvin I. Dunn Award for the best presentation in Cardiology at the meeting of the American College of Chest Physicians in Toronto, etc. Author of the 4 rationalization proposals and more 300 published works as sole author, including 27 books (of which 16 are in paper formats, 11 — in electronic versions), manuals and reference books.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.394
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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