{"id":"W94139903","doi":"10.11485/iteac.2013.0_1-4-1","title":"1-4 時空間特徴の共起を考慮した人物動作認識(第1部門画像処理と応用)","year":2013,"lang":"ja","type":"article","venue":"映像情報メディア学会年次大会講演予稿集 2013","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007540452,0.0004489492,0.0001864054,0.0008846384,0.003572034,0.003364086,0.0007282885,0.002747015,0.05039698],"category_scores_gemma":[0.001712453,0.0002272745,0.0004719842,0.0005838455,0.005769173,0.002628247,0.001657776,0.002375388,0.01081661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003129787,"about_ca_system_score_gemma":0.003411733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01495136,"about_ca_topic_score_gemma":0.01681426,"domain_scores_codex":[0.9992834,0.0001393734,0.00004370393,0.0001418611,0.0002552119,0.0001365865],"domain_scores_gemma":[0.9995162,0.000120236,0.00005711181,0.00005479216,0.0001967365,0.00005491389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003105973,0.00003909798,0.0009048298,0.00006914134,0.000007219197,0.0001886017,0.002470137,0.0001544286,0.0007966772,0.9397673,0.03095696,0.02461445],"study_design_scores_gemma":[0.00001393356,0.00007401818,0.003784075,0.0001519069,0.00002053359,0.0003959303,0.00291431,0.0002879587,0.002147421,0.2527922,0.7373799,0.00003774498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01060883,0.001251571,0.003726153,0.007302815,0.001420741,0.00007073906,0.0001192903,0.00005158617,0.9754483],"genre_scores_gemma":[0.2479771,0.001616696,0.005176695,0.00376686,0.000683528,0.0001386708,0.0001650488,0.00005823573,0.7404172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05039698,"threshold_uncertainty_score":0.1685947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007497128748484025,"score_gpt":0.1888838764964704,"score_spread":0.1813867477479864,"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."}}