{"id":"W1878631143","doi":"10.3847/0004-6256/151/2/36","title":"WISE PHOTOMETRY FOR 400 MILLION SDSS SOURCES","year":2016,"lang":"en","type":"article","venue":"The Astronomical Journal","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Waterloo","funders":"Lawrence Berkeley National Laboratory; York University; National Energy Research Scientific Computing Center; Carnegie Mellon University; Office of Science; Johns Hopkins University; College of Engineering, Michigan State University; Harvard University; Ohio State University; New Mexico State University; University of Portsmouth; Yale University; Vanderbilt University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; National Aeronautics and Space Administration; Princeton University; Brookhaven National Laboratory; U.S. Department of Energy","keywords":"Photometry (optics); Sky; Galaxy; Flux (metallurgy); Infrared; Near-infrared spectroscopy","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":[0.001235194,0.0007775927,0.0004704269,0.005286587,0.0005472362,0.000670568,0.0005895082,0.0004696614,0.01095286],"category_scores_gemma":[0.00313173,0.0005288518,0.001004854,0.00360073,0.0001956137,0.0006599476,0.001334411,0.0005848216,0.009173518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005021055,"about_ca_system_score_gemma":0.0003863893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003395003,"about_ca_topic_score_gemma":0.005727767,"domain_scores_codex":[0.9991204,0.00008395153,0.00007875829,0.0002579311,0.0003351966,0.0001237711],"domain_scores_gemma":[0.9979242,0.00018525,0.0005138897,0.0007105893,0.0004428035,0.000223225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005075164,0.000261167,0.375911,0.0007681024,0.0006940113,0.0003298133,0.000809452,0.006588252,0.03828562,0.005905519,0.403233,0.1667065],"study_design_scores_gemma":[0.0001156794,0.0001284664,0.7627751,0.0001453981,0.0002614976,0.0004933544,0.0002434436,0.006102339,0.01592924,0.004026599,0.2096626,0.0001163694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.429974,0.001845619,0.0345515,0.0009258558,0.0003365539,0.0004296433,0.4786699,0.01944139,0.03382554],"genre_scores_gemma":[0.2444873,0.0006834824,0.07524258,0.0008404333,0.0003289776,0.0007419036,0.666736,0.002610477,0.008328824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01095286,"threshold_uncertainty_score":0.03664094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460453494995187,"score_gpt":0.2780791355180222,"score_spread":0.2634746005680704,"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."}}