{"id":"W3041085779","doi":"10.1088/1742-6596/1525/1/012085","title":"Particle identification using semi-supervised learning in the PICO-60 dark matter detector","year":2020,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Snolab","funders":"","keywords":"WIMP; Dark matter; Physics; Weakly interacting massive particles; Detector; Massive particle; Particle physics; Supervised learning; Nuclear physics; Artificial intelligence; Computer science; Astrophysics; Scalar field dark matter; Dark energy; Cosmology; Artificial neural network; Optics","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":[0.001674191,0.0006728746,0.0006943925,0.0006538455,0.0004331446,0.0006248995,0.001314318,0.0008533139,0.000657863],"category_scores_gemma":[0.002386461,0.0002677908,0.0004517927,0.0003000145,0.0006002754,0.0005328786,0.0008722009,0.0007313514,0.0004428341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007056315,"about_ca_system_score_gemma":0.0009212514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005826102,"about_ca_topic_score_gemma":0.004484351,"domain_scores_codex":[0.9994071,0.0001999386,0.00002674091,0.0001678101,0.0001200407,0.00007842355],"domain_scores_gemma":[0.9982281,0.0008378339,0.0001722393,0.0002340911,0.0003870085,0.0001407471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007859444,0.0006588594,0.01702605,0.00007690149,0.0001190167,0.0001923818,0.0001178707,0.7569929,0.009216073,0.001956123,0.002205619,0.2106523],"study_design_scores_gemma":[0.000003973646,0.00001869118,0.0003967728,8.71251e-7,0.000001653462,0.000005842565,0.000005161678,0.9980841,0.001149642,0.0002687635,0.00006192859,0.000002712708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6801504,0.000220904,0.3130316,0.0002610919,0.00005850014,0.0001363395,0.0002132301,0.003237305,0.002690551],"genre_scores_gemma":[0.928978,0.00002340113,0.06870546,0.00006786857,0.00001470174,0.0000482334,0.0003888183,0.0000377007,0.00173577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005826102,"threshold_uncertainty_score":0.0115844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098084098984074,"score_gpt":0.2483178287875866,"score_spread":0.2173369877977459,"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."}}