{"id":"W2734704662","doi":"","title":"Big Data Usage in Terms of Market Recruitment: Possibilities and Limitations","year":2017,"lang":"en","type":"article","venue":"The Journal of Internet Banking and Commerce","topic":"Economic and Technological Developments in Russia","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Data science; Computer science; Big data; World Wide Web; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07606194,0.0009856847,0.001729632,0.005171886,0.001735469,0.009288707,0.006157519,0.002681091,0.01242384],"category_scores_gemma":[0.2604374,0.0006702448,0.001537979,0.01545195,0.002588623,0.01508193,0.005030635,0.00262664,0.002533758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002338765,"about_ca_system_score_gemma":0.004549408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01436364,"about_ca_topic_score_gemma":0.0125074,"domain_scores_codex":[0.9074773,0.06975603,0.005315074,0.004630022,0.01087585,0.001945795],"domain_scores_gemma":[0.532225,0.3867758,0.0140879,0.02338026,0.03858386,0.004947157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009933306,0.0007168588,0.4632069,0.005530763,0.001419911,0.0002064695,0.007473238,0.006606298,0.0005071736,0.08462812,0.0961376,0.3325733],"study_design_scores_gemma":[0.0001911041,0.000572999,0.378334,0.01130323,0.001021229,0.0009205183,0.03707455,0.09321544,0.003694514,0.1963063,0.2769267,0.0004394913],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4344036,0.05637017,0.06910346,0.2330133,0.007124384,0.00177721,0.04562692,0.0007880554,0.1517928],"genre_scores_gemma":[0.9509584,0.004817368,0.02244752,0.007378348,0.00239232,0.001613866,0.005767689,0.0001897803,0.00443479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07606194,"threshold_uncertainty_score":0.4022588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2847109054436782,"score_gpt":0.3617037935263767,"score_spread":0.07699288808269844,"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."}}