{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004735639,0.0006944947,0.000490997,0.002956337,0.0006129511,0.001849847,0.000954237,0.0005590903,0.03177346],"category_scores_gemma":[0.005264327,0.0002299325,0.0004345814,0.005202937,0.0002769684,0.001077391,0.0009587008,0.0007964559,0.02521811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629552,"about_ca_system_score_gemma":0.001788531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04928368,"about_ca_topic_score_gemma":0.050994,"domain_scores_codex":[0.998785,0.00006147528,0.00008231826,0.0001446961,0.0007820722,0.0001446441],"domain_scores_gemma":[0.9969937,0.0004350262,0.0003572299,0.0005691492,0.001334343,0.000310536],"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":[0.0006516135,0.0002815223,0.02901639,0.0004420742,0.00005593478,0.0003357536,0.0001701986,0.003212794,0.003159353,0.006101055,0.9070061,0.04956716],"study_design_scores_gemma":[0.0001323441,0.0000819427,0.0590795,0.0001052594,0.00001945617,0.0002655136,0.0003701356,0.008323312,0.004540585,0.001716943,0.9252875,0.0000773459],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0218719,0.0001830328,0.0009970716,0.0003856187,0.0001307041,0.0001816617,0.9458271,0.00157168,0.02885121],"genre_scores_gemma":[0.03753919,0.0001921964,0.002491481,0.0001857396,0.00006748362,0.0001935129,0.9498167,0.0002107224,0.009303056],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04928368,"threshold_uncertainty_score":0.1062928,"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."}}