{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000937044,0.0002778375,0.000385317,0.0003860595,0.0004245807,0.0007379604,0.00167395,0.0006026085,0.004854284],"category_scores_gemma":[0.004084594,0.0002603247,0.0002888316,0.0006311312,0.000444599,0.001317698,0.0007700389,0.0007656909,0.000978324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004846987,"about_ca_system_score_gemma":0.001466949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004238384,"about_ca_topic_score_gemma":0.00479796,"domain_scores_codex":[0.9993992,0.0002034186,0.00004674374,0.0001193833,0.0001367038,0.00009470234],"domain_scores_gemma":[0.9982982,0.0008538431,0.00008066528,0.0003781515,0.000329344,0.00005978691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000684882,0.0003085833,0.002820109,0.0002210443,0.00005612529,0.0001593755,0.0005148965,0.1500947,0.03693771,0.04066514,0.008111169,0.7594261],"study_design_scores_gemma":[0.00006054312,0.0002351967,0.0004430044,0.00001943359,0.00004411933,0.00007402895,0.00007716366,0.9450974,0.02202141,0.02533629,0.006575759,0.00001555739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03471097,0.000272107,0.9583793,0.0001567898,0.00003849051,0.00009758184,0.00007687996,0.002739997,0.003527841],"genre_scores_gemma":[0.4462405,0.0001322291,0.5467894,0.000160684,0.00002474936,0.0002264996,0.0002412292,0.0002417732,0.005942886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004854284,"threshold_uncertainty_score":0.01623923,"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."}}