{"id":"W26358119","doi":"10.2316/journal.206.2009.3.206-3269","title":"DYNAMIC EVENT INTERPRETATION AND DESCRIPTION FROM VISUAL SCENE BASED ON COGNITIVE ONTOLOGY FOR RECOGNITION BY A ROBOT","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Interpretation (philosophy); Computer science; Ontology; Event (particle physics); Artificial intelligence; Cognition; Robot; Semantic interpretation; Computer vision; Human–computer interaction; Natural language processing; Programming language; Psychology; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001654819,0.0001215051,0.0001752921,0.000199256,0.00003656741,0.0001182537,0.0000574571,0.00008466554,0.000004396823],"category_scores_gemma":[0.00006828302,0.0001177878,0.00005108138,0.00004020943,0.00001587711,0.0002432987,0.000004426985,0.00009676769,0.000001485535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000997098,"about_ca_system_score_gemma":0.0000147709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004190915,"about_ca_topic_score_gemma":0.000004272079,"domain_scores_codex":[0.9991346,0.0000330526,0.0004249772,0.0001067387,0.0002071171,0.00009350394],"domain_scores_gemma":[0.9992663,0.0001323378,0.0002286979,0.00002892637,0.0002923613,0.00005141137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003537568,0.0002195672,0.0002408625,0.00004939301,0.0002282172,0.00001046016,0.0003820623,0.5561544,0.03362916,0.0001324721,0.0002999762,0.4082997],"study_design_scores_gemma":[0.001212217,0.0004215063,0.007264164,0.0005297168,0.0000515778,0.00002515546,0.00006571793,0.9874396,0.00101968,0.001840652,0.00001300447,0.000116978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2688645,0.0002291621,0.7296325,0.000400162,0.0006329548,0.0001322161,0.00004509089,0.00003667439,0.00002672351],"genre_scores_gemma":[0.990805,0.0001321751,0.008592978,0.0001482161,0.00007588252,0.000003859358,0.0002247889,0.00001254758,0.000004603417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7219405,"threshold_uncertainty_score":0.4803249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009048685298226538,"score_gpt":0.2623630698648263,"score_spread":0.2533143845665998,"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."}}