Bibliographic record
Abstract
The biennial, international Algorithmic Number Theory Symposium (ANTS) provides the premier international forum for state-of-the-art research in computational and algorithmic number theory.This conference is devoted to algorithmic aspects of all branches of number theory, including elementary number theory, algebraic number theory, analytic number theory, geometry of numbers, arithmetic algebraic geometry, finite fields, and cryptography.ANTS-XIII, the thirteenth meeting in the Algorithmic Number Theory Symposia series, was held July 16-20, 2018, at the University of Wisconsin-Madison.This volume contains the 28 contributed papers that were presented at the conference; each paper was presented by one of the paper's authors.These 28 papers were selected from 48 submissions through a double-blind refereeing process, where the program committee solicited a minimum of two expert referees for each paper.In addition to the contributed papers, the conference featured five invited plenary speakers, a poster session on the afternoon of July 17, and a rump session on the afternoon of July 19.The organizing committee encouraged participation by women and underrepresented minorities.The 109 people who attended represented 13 countries.About 38% of the attendees were graduate or undergraduate students, and about 26% identified as female.Details about ANTS-XIII, such as the conference schedule, talk slides, abstracts of talks and posters, and more can be found on the conference website at http://www.math.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.440 | 0.311 |
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".