{"id":"W2081892160","doi":"10.3166/isi.19.3.93-105","title":"Big data analytics – Retour vers le futur 3. De statisticien à data scientist","year":2014,"lang":"fr","type":"preprint","venue":"Ingénierie des systèmes d information","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistician; Big data; Data science; Completeness (order theory); Variety (cybernetics); Computer science; Analytics; Data analysis; Order (exchange); Volume (thermodynamics); Data mining; Statistics; Mathematics; Business; Artificial intelligence; Physics","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":[0.03143444,0.001531739,0.001882739,0.005201918,0.002430009,0.01501258,0.001770894,0.006405241,0.003606784],"category_scores_gemma":[0.03704058,0.0008393128,0.001867893,0.004786896,0.02491898,0.02817689,0.005686017,0.01612914,0.002506813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007013536,"about_ca_system_score_gemma":0.005302511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004361701,"about_ca_topic_score_gemma":0.002385483,"domain_scores_codex":[0.977768,0.01185409,0.001196318,0.001976531,0.006632169,0.0005727571],"domain_scores_gemma":[0.964206,0.02591394,0.001002743,0.003437651,0.004290308,0.001149319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000703499,0.0000330361,0.0006434063,0.0004676784,0.00005177348,0.0001267653,0.001303231,0.0008345049,0.0004460023,0.8952202,0.03752032,0.0632827],"study_design_scores_gemma":[0.00002280509,0.00004975099,0.000505266,0.0008566653,0.00001713282,0.0002729919,0.0006127983,0.002383015,0.0004306199,0.5806704,0.4141018,0.00007671356],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.004407914,0.361538,0.08349187,0.501253,0.02416861,0.00003799442,0.0004389645,0.0006859628,0.02397775],"genre_scores_gemma":[0.2184509,0.3748174,0.1206274,0.09481911,0.1496524,0.0002831616,0.000593866,0.002371507,0.03838427],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03143444,"threshold_uncertainty_score":0.1662431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09202070383690884,"score_gpt":0.2954372656307753,"score_spread":0.2034165617938664,"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."}}