Leveling Up Citizen Science for (meta)genomic research
Bibliographic record
Abstract
Over the past decade, citizen science computer games have become a popular practice for engaging the public in research activities. This methodology had a noticeable impact in molecular and cell biology, where millions of online volunteers contributed to the classification and annotation of scientific data, but also to solve advanced optimization problems requiring human supervision. Yet, despite promising results, the deployment of citizen science initiatives through academic/professional web pages faces serious limitations. Indeed, the volume of human attention needed to process massive data sets and make state-of-the-art scientific contributions rapidly outpaces the participation and availability of online volunteers. To overcome this challenge, citizen science must transcend its “natural habitat” and reach out to the entire gaming communities. To address this challenge, we propose to build partnerships with commercial video game companies that already assembled large communities of gamers. In this talk, we describe how this approach can transform the impact of citizen science in (meta)genomics. We discuss our experience from Phylo, an online puzzle for gene alignment, to Borderlands Science, a massively multiplayer online game for microbiome data analysis. We show how to embeds citizen science tasks into a virtual universe to engage new user bases. These principles have profound implications for future citizen science initiatives seeking to meet the growing demands of biology.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".