{"id":"W6946174898","doi":"10.25740/2zme-3q44","title":"2013 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly","year":2019,"lang":"en","type":"dataset","venue":"Stanford Digital Repository","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Deliverable; Observatory; Data set; Set (abstract data type); Solar observatory; Scientific instrument; Data processing","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":[0.002104945,0.002176887,0.001532235,0.003467262,0.001083151,0.001415214,0.004113754,0.001836146,0.02006008],"category_scores_gemma":[0.008219514,0.000453824,0.001378751,0.005607677,0.0005405168,0.00122162,0.001713406,0.00312187,0.04118903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001926696,"about_ca_system_score_gemma":0.003239935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03339249,"about_ca_topic_score_gemma":0.04716744,"domain_scores_codex":[0.9982176,0.0003117649,0.0002609904,0.0004164935,0.0005980258,0.0001949906],"domain_scores_gemma":[0.9965228,0.0009111243,0.000278822,0.0007042332,0.001299191,0.000283944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004917197,0.00006882194,0.001525188,0.0002423092,0.0000378553,0.00002983398,0.00001356327,0.0007707502,0.000106883,0.000300741,0.9920026,0.004852258],"study_design_scores_gemma":[0.0005266573,0.00008846344,0.02159367,0.0003549244,0.00009312084,0.0001913728,0.0001902958,0.007324302,0.001728135,0.003716316,0.9640964,0.00009620407],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00105019,0.0001303486,0.0004212688,0.0002223131,0.00008174995,0.0000518124,0.9965593,0.0005694508,0.000913523],"genre_scores_gemma":[0.0009111065,0.00004286266,0.0006344061,0.00005000363,0.00001085125,0.000128924,0.9977785,0.00003844533,0.0004048279],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03339249,"threshold_uncertainty_score":0.06710768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219619606609045,"score_gpt":0.3723012397407552,"score_spread":0.2503392790798507,"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."}}