{"id":"W3122290629","doi":"10.1287/msom.2020.0923","title":"NetEase Cloud Music Data","year":2020,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloud computing; Sample (material); Impression; Computer science; Revenue; Set (abstract data type); Data set; Phone; World Wide Web; Advertising; Business; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002240511,0.0002133379,0.0001585759,0.00007426713,0.0004556254,0.0008712842,0.002241726,0.00003560656,0.0000861082],"category_scores_gemma":[0.00001004847,0.0002131378,0.00003599521,0.0003421574,0.0000135779,0.0007977277,0.00246091,0.0001622792,0.0005694589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003504564,"about_ca_system_score_gemma":0.00002349803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001546659,"about_ca_topic_score_gemma":0.0001461251,"domain_scores_codex":[0.9981707,0.00006527908,0.0002834728,0.0008340917,0.0003099034,0.0003365352],"domain_scores_gemma":[0.9976781,0.00002092501,0.00004366662,0.002018299,0.00004042799,0.0001985627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002879559,0.0003726092,0.00006677002,0.000980073,0.0004346995,0.0005613129,0.01059671,0.6029855,0.001136022,0.04827611,0.0654109,0.2691505],"study_design_scores_gemma":[0.0005541285,0.00003903211,0.0008719767,0.00005418139,0.00005923053,0.00001022966,0.0005346958,0.8258706,0.00374081,0.0001687647,0.1675959,0.0005004134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1180149,0.0001005235,0.8312367,0.03630746,0.0009314377,0.0007971731,0.00001701129,0.001089725,0.01150507],"genre_scores_gemma":[0.922461,0.00001460918,0.05731968,0.01927194,0.0003749454,0.00002418508,0.00009271643,0.00002489961,0.0004159929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8044462,"threshold_uncertainty_score":0.8691505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510427899530818,"score_gpt":0.2400433469400811,"score_spread":0.1849390679447729,"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."}}