Bibliographic record
Abstract
Morton K. Schwartz, PhD, FACB, is attending clinical chemist, chairman of the department of clinical laboratories, and head of the laboratory of applied and diagnostic biochemistry at Memorial Sloan Kettering Cancer Center in New York City. He has served as president of AACC, as president of the National Registry in Clinical Chemistry, as chairman of the Food and Drug Administration Clinical Chemistry and Hematology Panel, as a member of the National Cancer Institute immunodiagnosis study section and of numerous National Institutes of Health ad hoc study sections, as secretary of the National Committee on Health Laboratory Services, as secretary of the Committee of Scientific Society Presidents, as a member of the executive committee of the Academy of Clinical Laboratory Physicians and Scientists, as a member of the board of directors of the Board of the Registry of the American Society for Clinical Pathology, as chair of the 1973 and 1983 AACC annual meetings, and as chair of the AACC’s New York Metropolitan Section on two occasions. He has also served on the board of editors of numerous publications. Dr. Schwartz is the author or co-author of more 385 articles and has been co-editor of Advances in Clinical Chemistry and editor of a series of books on clinical and biochemical analysis.
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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.191 | 0.166 |
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".