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Record W6986324327

Paul D. Choy: A Life for Learning

2004· article· en· W6986324327 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsnot available
Fundersnot available
KeywordsSeveranceJurisprudencePublishingOrder (exchange)Medical schoolBiography
DOInot available

Abstract

fetched live from OpenAlex

Paul D. Choy was born on February 26th. 1896. He spent his childhood in Japan and America, and he returned to Korea when he turned twenty one years old. He graduated from Severance Union Medical College in 1921. After graduating the college, he went to Peking Union Medical College to study parasitology. He came back to Korea after one year as the first parasitologist in Korea. On returning, he took the charge of the clinical laboratory of Severance Hospital. Before long he made another journey for study to Canada. He spent two years in Toronto University studying pathology. After studying pathology, he challenged a new field of medicine. It was medical jurisprudence. He stayed two years in Japan in order to earn his doctorate in medical jurisprudence in Tohoku Imperial College. This time he returned as the first specialist in medical jurisprudence in Korea. His field of study was not confined to medical field. He had deep interest in current situation in Manchuria and Mongol, and wrote a book on this matter. His interest also extended to the history of ancient Korean people. He made extensive studies on this subject, which resulted in publishing a huge work on the origin of Korean people and its ancient history. He was a true pioneer of medicine in Korea and his life was characterized by endless quest for learning.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0100.011
Open science0.0010.008
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0210.014

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 head0.354
GPT teacher head0.542
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2004
Admission routes1
Has abstractyes

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