{"id":"W4385841878","doi":"10.5121/csit.2023.131316","title":"Classifying Galaxy Images Using Improved Residual Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Galaxy; Residual neural network; Task (project management); Class (philosophy); Residual; Contextual image classification; Field (mathematics); Pattern recognition (psychology); Artificial neural network; Image (mathematics); Astrophysics; Mathematics; Physics; Algorithm; Engineering","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":[0.0007007552,0.001025884,0.000537521,0.001316358,0.0002270332,0.0006832595,0.001181726,0.0006398336,0.001514292],"category_scores_gemma":[0.00133164,0.0002432289,0.0007272457,0.0005303518,0.0003164994,0.0009765807,0.0005584052,0.0008393332,0.001109027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007019506,"about_ca_system_score_gemma":0.0004146041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01330659,"about_ca_topic_score_gemma":0.01473678,"domain_scores_codex":[0.9996146,0.00007624418,0.00001687499,0.0001299005,0.00009304147,0.00006921063],"domain_scores_gemma":[0.9995412,0.00009685389,0.00005383979,0.00007865377,0.0001990784,0.00003042311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006388979,0.0004159119,0.009889572,0.0001142222,0.0002026328,0.0002051311,0.0001229718,0.2422989,0.03229071,0.003081475,0.01186837,0.6988713],"study_design_scores_gemma":[0.000006439419,0.00004552607,0.001171673,0.0000040625,0.00001460232,0.00002195032,0.00001666535,0.9946485,0.002709912,0.0006844811,0.0006695576,0.000006631611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.519311,0.00271096,0.4553469,0.001063843,0.0003304073,0.0002025582,0.001162805,0.008587504,0.01128403],"genre_scores_gemma":[0.9131224,0.0004545019,0.07593904,0.000309622,0.0001239917,0.00005569415,0.002887592,0.0001083482,0.006998838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01330659,"threshold_uncertainty_score":0.02645826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04235856025784004,"score_gpt":0.2826400353227368,"score_spread":0.2402814750648968,"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."}}