{"id":"W4298334798","doi":"","title":"Representation, Analysis and Recognition of 3D Humans:A Survey","year":2018,"lang":"en","type":"preprint","venue":"LillOA (Université de Lille (University Of Lille))","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Representation (politics); Computer science; Natural language processing; Pattern recognition (psychology); Artificial intelligence; Psychology; Political science; Politics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005529741,0.0002808097,0.0007344613,0.001444231,0.000467435,0.00004660318,0.0009634012,0.0003602586,0.0008100285],"category_scores_gemma":[0.00004838387,0.0003973451,0.0003801928,0.001551313,0.0003492622,0.000630079,0.001888363,0.0003089735,0.00003942851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002026147,"about_ca_system_score_gemma":0.0001313945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006178845,"about_ca_topic_score_gemma":0.004244367,"domain_scores_codex":[0.9977311,0.0004097153,0.0002878599,0.0008603875,0.0004199258,0.0002909586],"domain_scores_gemma":[0.9971317,0.000213119,0.0008947952,0.0008527093,0.0007228976,0.0001847554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003038928,0.003062013,0.3985132,0.003601346,0.03145389,0.00108857,0.1594534,0.002299206,0.009667248,0.005197616,0.05204694,0.3305776],"study_design_scores_gemma":[0.003452212,0.0006145057,0.9427903,0.0004523694,0.003578796,0.00003999265,0.005720763,0.02790611,0.003897174,0.007365688,0.00232281,0.001859256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89555,0.0001227514,0.09563214,0.0002380743,0.0002185083,0.0002843622,0.0003433173,0.0001234279,0.007487433],"genre_scores_gemma":[0.9693522,0.00123854,0.02549886,0.00005825277,0.00005618676,5.506991e-7,0.0007971587,0.00001968248,0.002978553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5442771,"threshold_uncertainty_score":0.9998478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248324535970266,"score_gpt":0.2272570747282454,"score_spread":0.1947738293685427,"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."}}