iGEM Toronto: Promoting Independent Undergraduate Research in Synthetic Biology at the University of Toronto
Bibliographic record
Abstract
iGEM Toronto is a multidisciplinary undergraduate research team at the University of Toronto, St. George campus, with a focus on designing and executing research projects in the field of synthetic biology. We are one of over 300 teams from across the globe that compete in the largest annual synthetic biology competition in the world, the Giant Jamboree, hosted by iGEM HQ in Boston, MA. With guidance from our graduate advisors, our team of undergraduate students independently design and execute a synthetic biology-based research project that not only involves work in the wet and dry lab, but also investigates the ethical and social implications of our research in society. We strive to promote this field of study, not often exposed to undergraduates, and provide the opportunity to conduct physical research at the university. Our collaboration with the URNCST Journal also allows our group to offer an opportunity to be published, a rare and valuable experience for undergraduate students. Altogether, iGEM Toronto has the goals of expanding the synthetic biology community in Toronto and encouraging the pursue of independent undergraduate research at the University of Toronto.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.023 |
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".