Proceedings of the Ninth HDL (High-Density Lipoprotein) Workshop: Focus on Cardiovascular Disease.
Bibliographic record
Abstract
The HDL (high-density lipoprotein) Workshop was established in 2009 as a forum for candid discussions among academic basic scientists, clinical investigators, and industry researchers about the role of HDL in cardiovascular disease. This ninth HDL Workshop was held on May 16 to 17, 2019 in Boston, MA, and included outstanding oral presentations from established and emerging investigators. The Workshop featured 5 sessions with topics that tackled the role of HDL in the vasculature, its structural complexity, its role in health and disease states, and its interaction with the intestinal microbiome. The highlight of the program was awarding the Jack Oram Award to the distinguished professor emeritus G.S. Getz from the University of Chicago. The tenth HDL Workshop will be held on May 2020 in Chicago and will continue the focus on intellectually stimulating presentations by established and emerging investigators on novel roles of HDL in cardiovascular and noncardiovascular health and disease states.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.025 |
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".