Efficient Label Gathering for Machine Training:Results from Muon Hunter 2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2017, the Muon Hunter project on the Zooniverse.org citizen science platform successfully gathered more than two million classification labels for nearly 140,000 camera images from VERITAS. The aim was to select and parameterize muon events for use in training convolutional neural networks. The success of this project proved that crowdsourcing labels for IACT image analysis is a viable avenue for further development of advanced machine-learning algorithms. These algorithms could potentially lend themselves to improving class separation between gamma-ray and hadronic event types. Nonetheless, it took two months to gather these labels from volunteers, which could be a bottleneck for future applications of this method. Here we present Muon Hunters 2.0: the follow-on project that demonstrates the development of unsupervised clustering techniques to gather muon labels more efficiently from volunteer classifiers.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it