Bibliographic record
Abstract
im Arthur's illustrious career, spread out over almost five decades, has contributed in a profound manner to our understanding of the theory of automorphic forms.He has single-handedly developed the trace formula, now known as the "Arthur-Selberg trace formula."This trace formula is a powerful tool in understanding the spectrum of reductive groups over number fields and Langlands's Program.In particular, it can be used to establish important cases of Langlands's Functoriality Principle, one of the central questions in the theory of automorphic forms and number theory.Some of its most general cases are proved by Arthur himself.Throughout his career he has received many honors, including the Canada Gold Medal for Science and Engineering (1999), the Wolf Prize in Mathematics ( 2015), and the Leroy P. Steele Prize of the AMS for Lifetime Achievement (2016).He was elected as a Fellow of the Royal Society of Canada (1980), a Fellow of the Royal Society of London (1992), a Foreign Honorary Member of the American Academy of Arts and Sciences (2003), and a Foreign Associate of the National Academy of Sciences (US) (2014).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.082 |
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".