Bibliographic record
Abstract
Machine learning has become a vital part of analysis in modern neutrino astronomy, and many recent discoveries would not be possible without it. This approach, however, is limited by the quality of available training data. Located at the South Pole, the IceCube Neutrino Observatory is a neutrino detector sensitive to astrophysical neutrinos from GeV to PeV energies, with ongoing efforts to push the sensitivity down to 100 MeV for neutrinos from transient events. IceCube is dominated by massive backgrounds, detecting more than 10 billion atmospheric muons for each astrophysical neutrino, and machine learning is a powerful tool to reduce this large background rate. However, undetected outliers in labelled training data negatively affect the final performance of machine learning algorithms. Citizen scientists can help to quantify and qualify outliers in IceCube data to improve the detection of such outliers. In this contribution, we present the ongoing efforts of utilising citizen science to improve a machine-learning-based event selection targeting sub-GeV astrophysical neutrinos.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".