Bibliographic record
Abstract
This article is the autobiography of Keith L. Moore, PhD, DSc, FIAC, FRSM, Professor Emeritus of Anatomy and Cell Biology at the Faculty of Medicine, University of Toronto. It is our great pleasure to have the autobiography of a pioneer of clinical anatomy in our journal, IJAV. I (Tunali S.) have been first acquainted with Dr. Moore’s great work during my intership in the medical school. Unfortunately, Clinically Oriented Anatomy was not widely accessible in Turkey when I was having my anatomy courses. This late acquaintance (better than never) left an indelible impression on my career. It was amazing to see that complicated anatomical information was put into service in an easy to understand way. Undoubtedly, Dr. Moore had influenced my career choice, even he may not be aware of it. As countless anatomists worldwide, I continually refer his books and suggest them to my students. It is a great honor to publish this article as it may be regarded as a debt of gratitude. Please enjoy the autobiography of Dr. Moore. Keith L. Moore was born on October 5th, 1925 in Brantford, Ontario, Canada. He worked for over 60 years as a Clinical Anatomist. His books are of major references in the area of anatomy and embryology for medical students and anatomists alike.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.034 |
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".