{"id":"W2806202122","doi":"","title":"MSR System Description - TAC 2016 KBP Cold Start Slof Filling Track.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Computer science; Operating system","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":[0.003887505,0.001723587,0.001166084,0.003796572,0.001039248,0.00394241,0.00333495,0.001945304,0.1187007],"category_scores_gemma":[0.01775743,0.001131808,0.001137495,0.002957724,0.0004284716,0.004650775,0.0022069,0.002138144,0.1513276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169556,"about_ca_system_score_gemma":0.004798286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02844369,"about_ca_topic_score_gemma":0.01793228,"domain_scores_codex":[0.9964771,0.0006004242,0.0005859441,0.0004987152,0.001522051,0.0003158018],"domain_scores_gemma":[0.988914,0.001950007,0.0006745597,0.003040855,0.004979141,0.0004414708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006139596,0.00008762421,0.0009752361,0.001117314,0.00003783306,0.0002437711,0.0003078289,0.002058217,0.003613193,0.005392118,0.9482864,0.03726657],"study_design_scores_gemma":[0.0001954385,0.00009281508,0.001683437,0.0003974394,0.00003927868,0.0002855105,0.0001958028,0.01139165,0.01014382,0.004339172,0.9711284,0.0001072294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.004593736,0.0003971143,0.07851337,0.001459616,0.0005114496,0.0009385151,0.5855309,0.2803752,0.04768006],"genre_scores_gemma":[0.01890093,0.0002616833,0.0494072,0.0007482228,0.00009837026,0.001008517,0.8753616,0.02898137,0.02523221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1187007,"threshold_uncertainty_score":0.3970932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05224603676120076,"score_gpt":0.3066374876255397,"score_spread":0.254391450864339,"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."}}