Canada-China Workshop on Industrial Mathematics K.C. Chang (Peking University),
Bibliographic record
Abstract
About 10 young Chinese mathematicians were invited by MITACS, and selected by MCME from top Chinese universities to joint MITACS teams and work with Canadian researchers, for a period of six months. The purpose of this program was to jump-start the Chinese industrial mathematics program by training young and promising Chinese mathematicians in a collaborative environment such as the one found within a MITACS projects, where a team of mathematical scientists carry out applied researches relevant to industry. All the participants of the program found the experience rewarding and expressed strong desire to continue collaborations started by this pilot program. The Canada-China Workshop in Industrial Mathematics was organized after the successful conclusion of the MITACS-MCME pilot program. The objectives of the workshop are to provide a platform for the participants to 1. exchange ideas and insights on the development of industrial mathematics in both countries; 2. assess the success of existing collaboration between the two countries; 3. discuss future directions. In order to achieve these goals, we have invited prominent mathematical scientists as well as young researchers in both countries to show case their researches at the BIRS workshop. Round-table discussions were also organized for the participants to provide their insights and exchanges ideas on the development of industrial mathematics program in both countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".