Community-wide hackathons to identify central themes in single-cell multi-omics
Bibliographic record
Abstract
Biological systems are fundamentally multi-scale, with mostly uncharacterized molecular pathways, cellular actions, and cellular communities that collectively give rise to their function. While one high-throughput measurement technology can resolve specific biological molecules, comprehensive characterization of biological systems can only be achieved by integration of multi-modal data types across molecular, cellular, spatial, and population scales. The integration of heterogeneous and complementary assays from multi-omics can reveal interactions between modalities that drive biological systems and processes. Recent advances in single-cell multi-omics technologies provide unprecedented opportunities for such multi-scale characterization but interpreting biological processes from these data requires parallel advances in novel computational techniques.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.066 | 0.064 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".