Proceedings of the Poster Session and Student Colloquium Symposium
Bibliographic record
Abstract
Building on a key goal of the Multi-conference -to bring Modeling and Simulation practitioners and researchers coming from various domains together- the Poster Session offers a single point of encounter and discussion for scientific ideas in embryonic state and with high potential. Interesting novel results, original ideas or works-in-progress that are not quite ready for a regular full-length paper find their place in the Poster Session. Presenters and attendees have the opportunity to engage in enriching discussions about their work in a cross-domain environment. This year we are very excited to introduce also a Student Colloquium. The objective of the Colloquium is to give an opportunity for students (in particular Ph.D. students) to showcase and discuss their work in progress during a session of short oral presentations. Any student attending the conference can participate, including students with accepted papers at the Multi-Conference. For those students with an accepted paper, they can choose to give a short version of such paper during the Colloquium, including new results or research advances since the original submission. Also, all Colloquium students will participate in the Poster Session. This way, students at both early and advanced stages of their careers will find the occasion to network with colleagues, give their work a wider visibility, and brainstorm ideas strengthening their fitness in scientific discussion.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.266 | 0.146 |
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".