{"id":"W4402263027","doi":"10.1051/0004-6361/202450381","title":"Classifying binary black holes from Population III stars with the <i>Einstein</i> Telescope: A machine-learning approach","year":2024,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"H2020 Marie Skłodowska-Curie Actions; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; National Centres of Competence in Research SwissMAP; National Research Centre; Aspen Center for Physics; European Research Council; Fondazione Cariplo; National Science Foundation","keywords":"Physics; Astrophysics; Binary number; Stars; Telescope; Astronomy; Population; Binary star; Einstein; Binary black hole; Mathematical physics; Gravitational wave","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":[0.0001374895,0.0005030865,0.0004212077,0.00008245376,0.0006396544,0.0006050951,0.0003485091,0.00005583041,0.00009575384],"category_scores_gemma":[0.000001571381,0.000345759,0.0001935589,0.0003956035,0.0005741879,0.0006697705,0.0002637231,0.001170107,0.00006148077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004682489,"about_ca_system_score_gemma":0.0001156989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005523501,"about_ca_topic_score_gemma":0.000003161059,"domain_scores_codex":[0.9976433,0.000159072,0.0003139825,0.0007786423,0.0003973006,0.0007076967],"domain_scores_gemma":[0.9990424,0.000185178,0.0001305168,0.0003706612,0.00004989718,0.0002213808],"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":[0.0001536387,0.0001939284,0.01381189,0.00002541021,0.0004481343,0.000006807838,0.0005988157,0.007944759,0.002013761,0.01088979,0.0001566957,0.9637564],"study_design_scores_gemma":[0.01168007,0.004300213,0.06691683,0.002197343,0.002137015,0.00001202805,0.0468149,0.3824612,0.012395,0.004951249,0.4595252,0.006608941],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.457634,0.0001285529,0.540457,0.0001776636,0.00004310683,0.0003031579,0.000107135,0.00008582376,0.001063582],"genre_scores_gemma":[0.9712681,0.000004655222,0.02644082,0.00001814526,0.001020255,0.00007864944,0.0007209243,0.0000748481,0.0003736589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9571474,"threshold_uncertainty_score":0.9998994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087309520960759,"score_gpt":0.229528544803131,"score_spread":0.2186554495935234,"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."}}