2024-2025 SSGSA STEM Sustainability Case Competition: Regenerative Science
Bibliographic record
Abstract
The SSGSA STEM Sustainability Case Competition is an annual research case competition hosted by undergraduate students from the STEM Students Guelph Support Association (SSGSA). The mission of this competition is to provide University of Guelph undergraduate students with an opportunity to develop their own research proposal while gaining valuable experience in innovative thinking and critical research analysis. Each year, students in teams of up to three are paired with an experienced mentor to develop and present a novel research proposal aligning with the competition’s theme. During the competition, students are taught fundamental principles outlining three lab techniques that they could write about in their proposal. The theme of this year’s competition was Regenerative Science, and competitors learned about Bioremediation, PCR, and CRISPR-Cas9. In the 2024-2025 SSGSA STEM Sustainability Case Competition, over 55 participants submitted abstracts for evaluation by faculty judges. We present to you the top 19 winning submissions in our competition abstract booklet. We hope you enjoy reading this year’s best abstract submissions and encourage you to participate in the growing SSGSA community as we strive to encourage interest in novel scientific research fields surrounding STEM.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.198 | 0.087 |
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".