SciNapse 2024-2025 Undergraduate Science Case Competition: The Gut Microbiome
Bibliographic record
Abstract
The SciNapse Undergraduate Science Case Competition (USCC) offers undergraduates the chance to craft an innovative research proposal. In this competition, a case study is provided, and students conduct comprehensive literature reviews—including scholarly publications, reports, and studies—to identify and connect crucial elements, which then form the basis of a supporting hypothesis. They also design a methodology to assess the validity of their hypothesis. This year’s case focused on the intricate and often overlooked realm of the gut microbiome, exploring its significant effects on human health, disease, and wider ecological systems. In teams of 1-4, undergraduate students engaged with the challenge by crafting innovative research proposals aimed to catalyze breakthroughs and deepen our understanding of the intricate gut microbiome. In total, the 2024-2025 USCC attracted 626 undergraduate students from 14 universities across North America. The top 10% of written submissions in each division are highlighted in this abstract booklet. You may find more information on the annual SciNapse USCC on our website at https://scinapsescience.com.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.021 |
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".