{"id":"W4385720512","doi":"10.3389/fspas.2023.1197358","title":"Efficient galaxy classification through pretraining","year":2023,"lang":"en","type":"article","venue":"Frontiers in Astronomy and Space Sciences","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Simon Fraser University","keywords":"Physics; Galaxy; Convolutional neural network; Sky; Artificial intelligence; Astrophysics; Galaxy formation and evolution; Pattern recognition (psychology); Computer science","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":[0.0007383944,0.001157809,0.0005839412,0.0005500561,0.0003781412,0.0007257195,0.001567393,0.0009711568,0.00414385],"category_scores_gemma":[0.003463299,0.0005899899,0.0005117101,0.0006245206,0.0006335945,0.001628265,0.0007821579,0.001658296,0.002583354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548845,"about_ca_system_score_gemma":0.001297616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01088974,"about_ca_topic_score_gemma":0.02974265,"domain_scores_codex":[0.9996977,0.00004453289,0.00001703217,0.0001016805,0.00007399177,0.00006509623],"domain_scores_gemma":[0.9985279,0.0005786925,0.0001296986,0.0003962647,0.0003054743,0.00006198639],"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":[0.000571291,0.0005647456,0.02824484,0.0003503493,0.0001814303,0.0002062674,0.0001408831,0.256501,0.05075082,0.003745803,0.03071028,0.6280323],"study_design_scores_gemma":[0.00004484478,0.0002151593,0.01225147,0.00003885881,0.0000464006,0.0001163804,0.00007260937,0.9363586,0.03880513,0.004126748,0.007891928,0.00003190408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5762154,0.001994692,0.3789055,0.001665932,0.0005342282,0.0003161422,0.00292625,0.02450112,0.01294079],"genre_scores_gemma":[0.8388357,0.0004108076,0.1441898,0.0009080468,0.0001063535,0.0002582888,0.008298625,0.0003887249,0.006603689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01088974,"threshold_uncertainty_score":0.0216527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750100793306008,"score_gpt":0.2409585794344362,"score_spread":0.2234575715013762,"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."}}