Lessons Learned from Video Game Players Sorting Genomes
Bibliographic record
Abstract
The fields of human computing and citizen science have made a lot of progress in creating engaging applications and encouraging people to donate part of their time to solve problems that are difficult for computers but easy for them. One option that has been widely used in citizen science particularly is to transform the problem into smaller tasks that can be presented as a puzzle game. This way, volunteers can participate by playing a game and contribute to scientific advancement. This work presents the lessons learned from the citizen science game GeSort, with a focus on better understanding player strategies and puzzle difficulty. Matches played over one year were analyzed. We examined which player strategies led to optimal and sub-optimal solutions. We analyzed how certain repetitive patterns of shapes and colours could confuse players and cause mistakes. Moreover, we present a simple system for dynamically adjusting the difficulty in a puzzle game when an achievable score is known. Based on our results, we propose some design guidelines that can generally be applied to any puzzle game in order to alleviate the potential issues related to puzzle presentation and patterns.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".