{"id":"W4382403947","doi":"10.1093/rasti/rzad023","title":"A machine learning approach to galactic emission-line region classification","year":2023,"lang":"en","type":"article","venue":"RAS Techniques and Instruments","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"Natural Sciences and Engineering Research Council of Canada; College of Natural Resources and Sciences, Humboldt State University; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Fonds de recherche du Québec – Nature et technologies; Université de Montréal; National Research Council; Newton Fund; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Royal Society","keywords":"Doubly ionized oxygen; Astrophysics; Line (geometry); Luminosity; Emission spectrum; Physics; Photoionization; Supernova; Artificial intelligence; Planetary nebula; Machine learning; Artificial neural network; Computer science; Astronomy; Spectral line; Mathematics; Galaxy; Stars; Geometry; Ionization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001886872,0.0006990373,0.0005458133,0.001697791,0.0005227126,0.001028834,0.001612897,0.001154731,0.00106843],"category_scores_gemma":[0.005889848,0.0002854197,0.0006692057,0.001146322,0.0005997016,0.00101454,0.0007767773,0.001480184,0.0002433713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163504,"about_ca_system_score_gemma":0.0006319583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005009813,"about_ca_topic_score_gemma":0.004547978,"domain_scores_codex":[0.9990563,0.0004545358,0.00005892188,0.0002162867,0.0001470301,0.00006691978],"domain_scores_gemma":[0.9964018,0.002485051,0.000219851,0.0002967551,0.0005273503,0.00006913868],"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":[0.00007279011,0.0002892058,0.01068534,0.00007649866,0.0001345704,0.0001089916,0.00009771869,0.7950584,0.001600908,0.007199758,0.001856006,0.1828198],"study_design_scores_gemma":[0.000002376836,0.00001108784,0.0003576179,0.000003804627,0.000002717509,0.000006457123,0.000007767021,0.9953809,0.0002722061,0.003807842,0.000144759,0.00000249602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1664923,0.001140453,0.8246312,0.001061448,0.00008941507,0.0001841796,0.0006634465,0.001416591,0.004320994],"genre_scores_gemma":[0.7865935,0.0001832334,0.2102383,0.0001807305,0.000126037,0.0001957139,0.0008335554,0.00004726918,0.001601787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005009813,"threshold_uncertainty_score":0.00997889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341598067587988,"score_gpt":0.2567581827243963,"score_spread":0.2333422020485164,"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."}}