Strengthening Research Through Scientific Platforms: Highlights from the 5th Canadian Network of Scientific Platforms (CNSP) Conference
Bibliographic record
Abstract
The most recent national Canadian Network for Scientific Platforms (CNSP) Scientific Platform Meeting was held from November 20 to 22, 2023, at the Montreal Neurological Institute, "The Neuro," affiliated with McGill University. This conference, attended by 114 representatives from 44 scientific platforms, was structured around three themes. Day 1 focused on open science, emphasizing transparency and the free sharing of scientific discoveries to enhance research efficiency. Discussions included panel talks on initiatives and resources related to open science and featured award presentations for 2023 CNSP National Platform Scientist Award winners for both the Platform Administrator Award and Platform Scientist Award. The day concluded with a conference dinner. Day 2 highlighted the critical role of scientific platforms in advancing research by providing essential infrastructure and expertise by hearing about different successful national and international networks. This day featured a poster session and a networking reception. Day 3 offered a professional development workshop and tours of McGill University's leading scientific platforms, showcasing the expertise within the scientific platform community.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".