{"id":"W2788967596","doi":"10.48550/arxiv.1802.06459","title":"Structured Label Inference for Visual Understanding","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oracle (Canada); Simon Fraser University","funders":"","keywords":"Inference; Computer science; Categorization; Artificial intelligence; Graph; Pattern recognition (psychology); Exploit; Set (abstract data type); Multi-label classification; Machine learning; Theoretical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001291473,0.0002168361,0.0002049988,0.0002365003,0.0002664017,0.000203004,0.0007959874,0.0002431356,0.00006329687],"category_scores_gemma":[0.00003360007,0.000252249,0.0001186638,0.0002573708,0.00008556963,0.0004044162,0.0006745874,0.0002684893,0.00006145667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002762728,"about_ca_system_score_gemma":0.000162149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001691409,"about_ca_topic_score_gemma":0.00004451909,"domain_scores_codex":[0.9987029,0.00005420836,0.0001419265,0.0007630173,0.00006794864,0.0002699391],"domain_scores_gemma":[0.9989492,0.0001163595,0.0002020038,0.0004536169,0.0001706504,0.0001081255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008429621,0.0001609195,0.0007162117,0.000265472,0.0002292191,0.00005697168,0.0005476082,0.005759893,0.0003217449,0.9869132,0.002261318,0.002683119],"study_design_scores_gemma":[0.0006456904,0.0001149049,0.0002077012,0.00009233486,0.00004225696,0.000001476973,0.00008073421,0.4735917,0.0005660407,0.5239268,0.0003432497,0.0003871599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1239327,0.000006032745,0.8733549,0.00004207922,0.0008257734,0.0002712348,0.00002163422,0.0002095286,0.001336169],"genre_scores_gemma":[0.9963937,0.00002765089,0.002654089,0.000106697,0.0001788473,0.00000138058,0.00003549032,0.00001222145,0.0005899022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.872461,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1976341404461591,"score_gpt":0.2504935777222261,"score_spread":0.05285943727606696,"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."}}