{"id":"W4407930644","doi":"10.1093/mnras/staf336","title":"Finding radio transients with anomaly detection and active learning based on volunteer classifications","year":2025,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"DSI-NRF Centre of Excellence in Human Development, University of the Witwatersrand, Johannesburg; University of Cape Town; Breakthrough Prize Foundation; Cape Peninsula University of Technology; Universiteit Stellenbosch; European Commission; Department of Science and Innovation, South Africa; HORIZON EUROPE Framework Programme; University of the Western Cape; University of Pretoria; University of Warwick; Neurosciences Research Foundation; Science and Technology Facilities Council; National Research Foundation; Alfred P. Sloan Foundation","keywords":"Physics; Anomaly detection; Anomaly (physics); Astronomy; Volunteer; Remote sensing; Astrophysics; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004010672,0.0006646028,0.0009354103,0.003160698,0.0004292629,0.001633426,0.001796623,0.001150789,0.0009053339],"category_scores_gemma":[0.01331771,0.0002020125,0.000678206,0.001686911,0.0007347374,0.001845801,0.001340377,0.001076977,0.000772966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005784219,"about_ca_system_score_gemma":0.0005089859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003282281,"about_ca_topic_score_gemma":0.00279414,"domain_scores_codex":[0.9978606,0.0008718384,0.000112773,0.0005418988,0.0004346479,0.0001782713],"domain_scores_gemma":[0.988638,0.005670259,0.001501235,0.001970915,0.001715479,0.0005041014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001210037,0.001002848,0.2319911,0.0002083679,0.0002980015,0.0002986941,0.0007264378,0.2307653,0.009427861,0.00442588,0.008692361,0.5109531],"study_design_scores_gemma":[0.00001608167,0.0001182237,0.007211128,0.00001584026,0.00001716574,0.00009502707,0.0001298936,0.9843988,0.0031019,0.003636226,0.001243384,0.00001614104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5567189,0.0004342761,0.4347363,0.0007029968,0.0001536863,0.000144123,0.0006337418,0.003140736,0.003335218],"genre_scores_gemma":[0.9191573,0.00006320474,0.07833629,0.00007663793,0.00009319273,0.00004461617,0.001065104,0.00007431673,0.001089221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004010672,"threshold_uncertainty_score":0.02121073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006572756067793578,"score_gpt":0.196878965150558,"score_spread":0.1903062090827644,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}