{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004203489,0.0002057327,0.0002474989,0.00007155622,0.0004402657,0.0001436169,0.000602003,0.00003384502,0.000949564],"category_scores_gemma":[0.00002080273,0.0001034963,0.0003729313,0.00009627747,0.0002544316,0.0002353384,0.0001301771,0.0003613714,0.0001950204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006310681,"about_ca_system_score_gemma":0.0001221968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006982694,"about_ca_topic_score_gemma":2.095121e-7,"domain_scores_codex":[0.9984119,0.0000999589,0.0003354789,0.0002364218,0.0002243524,0.0006919003],"domain_scores_gemma":[0.9987065,0.0004725779,0.0001699227,0.0002601945,0.00008413905,0.0003067323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005279489,0.0002644999,0.08034647,0.000002360247,0.0002993559,4.702429e-7,0.0000874544,0.0001348748,0.02550096,0.007273619,0.00414834,0.8814136],"study_design_scores_gemma":[0.02259351,0.002767447,0.1861601,0.0005143548,0.0004803145,0.00003271124,0.003648697,0.002504032,0.4632194,0.05574246,0.2598102,0.002526793],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.678943,0.00001926261,0.3188237,0.001569849,0.0001289551,0.0001760114,0.00003579589,0.00001096084,0.0002924493],"genre_scores_gemma":[0.9937661,0.000001139926,0.001885564,0.00001755901,0.002882698,0.00004285325,0.000004495185,0.00002703681,0.001372528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8788869,"threshold_uncertainty_score":0.9999637,"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."}}