14th Meeting of the Canadian Number Theory Association
Bibliographic record
Abstract
The Canadian Number Theory Association (CNTA) was founded in 1987 at the International Number Theory Conference at Laval University (Quebec), for the purpose of enhancing and promoting learning and research in number theory in Canada and beyond. To advance these goals, the CNTA organizes bi-annual conferences that showcase new research in number theory, with the aim of exposing Canadian and international students and researchers to the latest developments in the field. The CNTA meetings are among the largest number theory conferences world-wide. The previous CNTA conferences were held in Banff (1988), Vancouver (1989), Kingston (1991), Halifax (1994), Ottawa (1996), Winnipeg (1999), Montreal (2002), Toronto (2004), Vancouver (2006), Waterloo (2008), Wolfville (2010), Lethbridge (2012) and Ottawa (2014). 2016 returns CNTA — almost — to its 1988 birthplace of Banff. In the year of its 50th birthday, the University of Calgary in Calgary (Alberta, Canada) is pleased to host the 14th meeting of the CNTA. A highlight of this event is a special session honouring our distinguished colleague Richard Guy, in celebration of his 100th birthday which will take place on September 30, 2016. An exceptional scholar and Professor Emeritus at the University of Calgary, Richard’s numerous and outstanding contributions to number theory have had a lasting impact on the field, and his collection of Unsolved Problems in Number Theory in particular has influenced research articles and inspired graduate theses for decades. The CNTA-XIV Organizers wish all conference participants a fruitful and enjoyable time in Calgary!
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.147 | 0.054 |
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".