{"id":"W2806775407","doi":"","title":"Looping Pipelining approach to Knowledge Base Population in Optimized Implementation.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Base (topology); Knowledge base; Parallel computing; Software pipelining; Mathematics; Artificial intelligence; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003414,0.00007587695,0.0001350019,0.0001066672,0.0003547932,0.0001402065,0.00038269,0.000024591,0.00000276406],"category_scores_gemma":[0.00005297708,0.00007289547,0.00002047942,0.0001051201,0.0000373422,0.0003422055,0.0001528618,0.00005920232,0.000004746595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001747097,"about_ca_system_score_gemma":0.00002177454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001486948,"about_ca_topic_score_gemma":0.000007795095,"domain_scores_codex":[0.9992906,0.00007902164,0.0002295451,0.0002090967,0.00006632046,0.0001254143],"domain_scores_gemma":[0.9992322,0.000102301,0.0001568803,0.0004016778,0.00006791929,0.00003906202],"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":[0.0000071329,0.00002099639,0.001645628,0.00002405661,0.00000405226,6.971104e-8,0.001810272,0.000626034,0.0001589637,0.964302,0.000003581427,0.03139721],"study_design_scores_gemma":[0.002827117,0.000202188,0.1027676,0.0003605979,0.00005502062,0.00001960676,0.02164391,0.0227061,0.02462447,0.800684,0.02265488,0.001454548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02407342,0.0001292296,0.9692056,0.00005463681,0.00004391085,0.0003126541,0.000001365656,0.00003002893,0.00614912],"genre_scores_gemma":[0.9782692,0.000006970232,0.02093881,0.00001260082,0.00005670785,0.0001457038,0.000005481617,0.000004933727,0.0005595951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9541958,"threshold_uncertainty_score":0.2972591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824648382710532,"score_gpt":0.3229189690498516,"score_spread":0.2946724852227462,"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."}}