NAYGN McMaster Chapter Nuclear Case Competition 2025: Alpha-Emitting Isotopes in Medicine
Bibliographic record
Abstract
North American Young Generation in Nuclear (NAYGN) is a non-profit organization founded in 1999 that is dedicated to empowering the next generation of nuclear professionals. The NAYGN McMaster, a student led chapter of the organization at McMaster University, organized this case competition to provide undergraduate students with the opportunity to engage in problem-based learning while tackling current issues in the nuclear industry. This competition fosters collaboration, thoughtful discussion, and innovation, while also offering participants the chance to grow personally and professionally. This year’s theme was “alpha-emitting isotopes in medicine.” Teams were tasked with identifying and describing the full production cycle of a specific alpha-emitting isotope, other than Actinium-225, including its sourcing, production methods, applications in medicine, and any associated challenges or innovations in the process. After a round of written submissions, the top eight teams presented their research to a panel of judges at the NAYGN Nuclear Case Competition Expo. The judges selected the top four teams, whose abstracts have been published in this abstract booklet. If you would like to learn more about NAYGN McMaster Chapter or the NAYGN Nuclear Case Competition, please visit our Instagram page (@naygn_mcmaster) or email us (naygn@mcmaster.ca). Disclaimer: The views expressed throughout this case competition and publication are solely those of the competition participants and do not reflect those of NAYGN McMaster, McMaster University, or any other organization.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.304 | 0.079 |
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".