{"id":"W2808496877","doi":"10.1051/0004-6361/201833617","title":"Photometric redshifts from SDSS images using a convolutional neural network","year":2018,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Agence Nationale de la Recherche; York University; Carnegie Mellon University; Office of Science; Johns Hopkins University; College of Engineering, Michigan State University; Harvard University; Ohio State University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; University of Arizona; Princeton University; Brookhaven National Laboratory; U.S. Department of Energy","keywords":"Galaxy; Redshift; Photometric redshift; Physics; Astrophysics; Convolutional neural network; Sky; Artificial intelligence; Pixel; Astronomy; Pattern recognition (psychology); Computer science; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009260356,0.0003179704,0.0003025514,0.0001012576,0.0005990203,0.0001490228,0.0002085724,0.00004325342,0.0004858763],"category_scores_gemma":[0.000003334842,0.000338049,0.0001175463,0.0005002586,0.0003178013,0.0007073547,0.0001611038,0.0002160643,0.00009302136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006470311,"about_ca_system_score_gemma":0.00009877452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005451258,"about_ca_topic_score_gemma":0.000002089748,"domain_scores_codex":[0.9983479,0.0000643067,0.0003672116,0.0004064938,0.0002234001,0.0005907061],"domain_scores_gemma":[0.9990357,0.00006148468,0.0002586077,0.0003001541,0.0001634223,0.0001806055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003250632,0.00008039282,0.987415,0.000003258391,0.0001308889,2.493477e-8,0.0001855292,0.0005529357,0.0004108711,0.003121647,0.0005105027,0.007556417],"study_design_scores_gemma":[0.0007848472,0.0001140336,0.9829298,0.00002190234,0.00007337153,0.000001051718,0.0002470255,0.007806232,0.000486288,0.004760604,0.002427284,0.0003475796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.877402,0.00006716269,0.1215261,0.00002887463,0.0003368227,0.000178275,0.0001612525,0.0000492832,0.0002501891],"genre_scores_gemma":[0.9401512,7.885264e-7,0.05604287,0.000006530268,0.003454132,0.00001629038,0.0002375632,0.00003058112,0.00006002733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06548324,"threshold_uncertainty_score":0.9999071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009839054141582,"score_gpt":0.2145791610795117,"score_spread":0.2044807705380959,"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."}}