President President Elect Local Organizing
Bibliographic record
Abstract
It is with great pleasure that we welcome everyone back to Michigan State University for the 21st International Conference in MR Angiography. Since being founded by a team lead by Dr. Jim Potchen in 1989, Michigan State University has been proud to host the annual meeting on three previous occasions, the last in 1994. The MR Angio Club annual meeting has spanned the globe from Asia, to Europe, to the USA and Canada as well as the Far East. This wide diversity in locality has been matched with an ever-growing diversity and ingenuity to produce ever-improving clinical and research applications of MR Angiography. The Local Organizing Team is excited about the excellent quality of the greater than 115 abstracts submitted to the 21st MR Angiography meeting. Both the oral presentations and the expanded poster section should provide very stimulating discussions typical of the MRA Club meetings. We are also proud to announce a Student Poster Award as part of this year’s meeting. MSU Kellogg Center will be an excellent facility for our scientific program with a large, comfortable stadium seating auditorium and hotel rooms to accommodate many of the
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.402 | 0.296 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".