{"id":"W2489930683","doi":"10.1016/s0262-4079(16)31369-0","title":"Internet giant crunches 600 million users' data","year":2016,"lang":"en","type":"article","venue":"The New Scientist","topic":"ICT Impact and Policies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Beijing; Revenue; China; The Internet; Proxy (statistics); Big data; Advertising; Business; Quarter (Canadian coin); Telecommunications; Internet privacy; Commerce; Computer science; Geography; World Wide Web; Finance; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001828974,0.00008248341,0.00006766487,0.00004410458,0.00005050932,0.00009200373,0.0007172993,0.00002356325,0.000281554],"category_scores_gemma":[0.00003224946,0.0000411888,0.00002045977,0.0001329459,0.00009100163,0.0002196828,0.0001628605,0.00004336184,0.0007851718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002612106,"about_ca_system_score_gemma":0.00001630642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002058637,"about_ca_topic_score_gemma":0.0001655861,"domain_scores_codex":[0.9994791,0.00001137847,0.00008832695,0.0000665452,0.0001281494,0.0002264719],"domain_scores_gemma":[0.9992462,0.00004860689,0.00001355547,0.0006094853,0.000008017994,0.00007408761],"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":[0.000003360762,0.000004018103,0.0002125861,0.000006494978,0.00001586092,9.133891e-7,0.001562858,0.00002654397,0.03000948,0.0008133106,0.9525592,0.01478531],"study_design_scores_gemma":[0.0003107845,0.00001882274,0.01045255,0.00008019769,0.0000193372,0.00002024821,0.0001166274,0.002042959,0.03194661,0.0004008259,0.9543998,0.0001912206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945811,0.001045546,0.01706064,0.006835005,0.004917921,0.0002124868,0.0002251048,0.0005050748,0.02338718],"genre_scores_gemma":[0.9673194,0.00005570612,0.00009839408,0.0001242589,0.0002989085,7.899648e-7,0.00001045563,0.000014977,0.03207707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02150839,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03779610051967867,"score_gpt":0.2596169838669601,"score_spread":0.2218208833472815,"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."}}