The fourth annual Carnegie Mellon Libraries hackathon for biomedical data management, knowledge graphs, and deep learning
Bibliographic record
Abstract
In October 2023, a group of 44 scientists hailing from several U.S. states, Canada, Poland, and Switzerland came together for a hybrid in-person and virtual hackathon. The event was jointly hosted by Carnegie Mellon University Libraries and DNAnexus, a California-based cloud computing and bioinformatics company. This collaborative effort revolved around the theme of “Data Management and Graph Extraction for Large Transformer Models in the Biomedical Space.” In the spirit of fostering collaboration, participants organized themselves into five teams, which ultimately resulted in the successful completion of four hackathon projects. These projects encompassed a wide range of topics, from detecting features contributing to virus susceptibility to validating models using knowledge graphs. Repositories for the hackathon projects are available at https://github.com/collaborativebioinformatics. We hope that the insights and experiences shared by these teams, as detailed in the following manuscript, will prove valuable to the broader scientific community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".