Sponsored by MSA • MAS • MSC • IMS
Bibliographic record
Abstract
joint meeting, taking place for the first time in Canada. This event promises to be a memorable new experience of science mixed with the “joie de vivre ” of Québec City. Founded in 1608 by French explorer Samuel de Champlain, Québec City was the center of New France, which at the time extended from the St. Lawrence River in the northeast right down to the Gulf of Mexico in the south. By the end of the 19th century, Québec City had taken on many of its current attributes: political capital, university town, center of culture, tourist destination, and regional services and trade hub. The city’s old quarter was declared a world heritage site by UNESCO in 1985. The majority of the 650,000 residents of the greater Québec City metropolitan region (more than 95%) are French-speaking. However, English is largely used in all commercial and tourism facilities. Québec City is home to over 27 museums and interpretation centers providing a window on a vast world of history, the arts, heritage treasures, culture, and science. To cap it all, Québec City boasts many historic sites, architectural landmarks, and breathtaking green spaces, including the Fortifications of Québec National Historic Site, the Citadel, the parliament buildings, the Battlefields
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.546 | 0.236 |
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".