{"id":"W4211198861","doi":"10.1051/0004-6361/202142444","title":"Exploring X-ray variability with unsupervised machine learning","year":2022,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cluster analysis; Computer science; Context (archaeology); Unsupervised learning; Artificial intelligence; Light curve; Artificial neural network; Dimensionality reduction; Anomaly detection; Pattern recognition (psychology); Self-organizing map; Grid; Transformation (genetics); Set (abstract data type); Machine learning; Data mining; Astrophysics; Physics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001604626,0.000329899,0.0003303165,0.00005173188,0.0009709543,0.00008064814,0.0002460418,0.000008699584,0.0004356232],"category_scores_gemma":[0.000001242248,0.0003189373,0.0001020935,0.0002687684,0.00009278179,0.0004145594,0.0003794624,0.0006387667,0.00001092327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005027633,"about_ca_system_score_gemma":0.00009792719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001610739,"about_ca_topic_score_gemma":4.230454e-7,"domain_scores_codex":[0.9983002,0.0001538183,0.0002430689,0.0005316965,0.0002687162,0.0005024896],"domain_scores_gemma":[0.9992415,0.00007458967,0.0001501775,0.0003275854,0.00004006941,0.0001660608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002469197,0.0007067781,0.0918935,0.00002003689,0.0003818942,0.00000562167,0.001320502,0.05216325,0.001815849,0.0439433,0.00002059979,0.8074818],"study_design_scores_gemma":[0.02437083,0.0115118,0.09456988,0.0001993274,0.001392514,0.00002611138,0.07155582,0.03844585,0.004710049,0.03411398,0.7100292,0.00907459],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7202141,0.00001418286,0.2773449,0.00006074066,0.00007689141,0.0001950105,0.00009642057,0.00005901531,0.001938813],"genre_scores_gemma":[0.9829214,0.000001568906,0.01610557,0.00002184001,0.0003096651,0.0002782946,0.000177361,0.00004871978,0.0001356294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7984071,"threshold_uncertainty_score":0.9999263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696369230133491,"score_gpt":0.1848700078744621,"score_spread":0.1679063155731272,"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."}}