Angelo Scanu Memorial
Bibliographic record
Abstract
A ngelo Scanu spent almost the whole of his illustri- ous scientific career at the University of Chicago.Actually, he had 2 distinguished careers there: one devoted to plasma HDL (high-density lipoprotein) and the other to Lp(a) (lipoprotein(a)).Angelo had come to the United States in 1955 on a Fulbright Scholarship, working with Irvine Page at the Cleveland Clinic and with Walter Hughes at the Brookhaven National Laboratories.It was at this time he began his pioneering work on lipoproteins, developing a delipidation procedure that yielded apolipoproteins in essentially a lipidfree state.This breakthrough opened up the field of apolipoprotein research.He came to the University of Chicago in 1961 to do his American internship in internal medicine-a second internship, having done the first one in Italy, where he had graduated cum laude from Sassari University Medical School in 1949.Angelo then joined the faculty in the Department of Medicine at the University of Chicago, where he remained until his retirement in 2011.For the first 25 years of his research career, his focus was HDL, including the isolation and characterization of the structures of its 2 major apoproteins, A-I and A-II.Using innovative physicochemical methods, Angelo and his colleagues elucidated the role of the apolipoproteins in HDL structure.As a visiting investigator at
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.237 | 0.111 |
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".