{"id":"W1998142505","doi":"10.1088/1475-7516/2015/01/038","title":"Machine learning etudes in astrophysics: selection functions for mock cluster catalogs","year":2015,"lang":"en","type":"article","venue":"Journal of Cosmology and Astroparticle Physics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"","keywords":"ROSAT; Physics; Cluster (spacecraft); Astrophysics; Selection (genetic algorithm); Galaxy; Galaxy cluster; Scalability; Artificial intelligence; Computer science; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0002789776,0.0001211805,0.0002269261,0.00006273247,0.0001318829,0.00002902043,0.00007228446,0.00002800524,0.000009514919],"category_scores_gemma":[0.00002027969,0.0001137324,0.00007033229,0.0001556448,0.00006044046,0.0004703498,0.00003028474,0.0002641089,0.000008012384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005455951,"about_ca_system_score_gemma":0.00008449022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000445174,"about_ca_topic_score_gemma":0.00000773719,"domain_scores_codex":[0.9991137,0.00007398455,0.0003603278,0.0001058365,0.000111406,0.0002347372],"domain_scores_gemma":[0.9992079,0.00006229524,0.000305221,0.00006187007,0.0002566456,0.0001060455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000178615,0.0001863229,0.978837,0.000006012104,0.00005741472,3.373034e-8,0.0005933198,0.01348792,0.0006391756,0.004793846,0.0001945012,0.001025829],"study_design_scores_gemma":[0.005067642,0.001509275,0.8355099,0.00002557888,0.0001220465,0.00003553721,0.001633572,0.0518258,0.001827326,0.1015982,0.0005888641,0.0002562943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9322637,0.00004192612,0.0672541,0.0001611214,0.0001320015,0.0001038147,0.000008461662,0.000008954231,0.00002596068],"genre_scores_gemma":[0.9975637,0.000001336173,0.001865562,0.000004499705,0.0004468978,0.00001059886,0.00002313673,0.00001338262,0.00007084088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1433271,"threshold_uncertainty_score":0.4637871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455884068829797,"score_gpt":0.2364511666572434,"score_spread":0.2218923259689455,"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."}}