{"id":"W2782660655","doi":"10.1145/3182179","title":"Representation, Analysis, and Recognition of 3D Humans","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Exploit; Representation (politics); Focus (optics); Human–computer interaction; External Data Representation; Artificial intelligence; Facial recognition system; Data science; Face (sociological concept); Taxonomy (biology); Machine learning; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004955815,0.0001294542,0.0002350074,0.0003908383,0.0007594978,0.00008117009,0.0009176184,0.00006531388,0.00001010662],"category_scores_gemma":[0.00005439347,0.0001364817,0.0000689744,0.001723751,0.0004374167,0.0001929384,0.0001007095,0.000182693,0.00001185403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348001,"about_ca_system_score_gemma":0.00002737286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001576698,"about_ca_topic_score_gemma":0.0001539833,"domain_scores_codex":[0.9987109,0.0002138627,0.0003968115,0.0003875776,0.0001396141,0.0001512392],"domain_scores_gemma":[0.9957798,0.001321932,0.0001949089,0.002307012,0.0003148885,0.00008142644],"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":[0.000002894496,0.0001726396,0.002341802,0.000008875543,0.0001620772,6.631408e-8,0.001098568,0.00005745652,0.0006208341,0.001120996,0.000005992598,0.9944078],"study_design_scores_gemma":[0.002009086,0.0004951437,0.2159037,0.0001478266,0.001053191,0.00003143486,0.0007379385,0.7211738,0.01286366,0.03698726,0.007425842,0.001171146],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01122416,0.0001651448,0.9868729,0.0008588826,0.00003751718,0.0002944078,0.00002231253,0.0001369973,0.0003876439],"genre_scores_gemma":[0.5358322,0.0003731149,0.4636214,0.00006241462,0.00002335342,0.00004874366,0.0000218904,0.000005400695,0.00001151557],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9932367,"threshold_uncertainty_score":0.5841519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05880161358033074,"score_gpt":0.3587469231175845,"score_spread":0.2999453095372537,"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."}}