{"id":"W4404999448","doi":"10.3390/info15120766","title":"Enabling Perspective-Aware Ai with Contextual Scene Graph Generation","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Contextual design; Artificial intelligence; Graph; Human–computer interaction; Perspective (graphical); Data science; Natural language processing; Object (grammar); 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":[],"consensus_categories":[],"category_scores_codex":[0.0001610296,0.00009009288,0.00006135379,0.0002264055,0.0001586378,0.0006642489,0.0002170437,0.00003859805,0.00001409943],"category_scores_gemma":[0.00002652269,0.00007468776,0.00002688872,0.0005163644,0.00002052585,0.003217768,0.00004195822,0.0001764977,0.0002903943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009592123,"about_ca_system_score_gemma":0.00008834465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004047268,"about_ca_topic_score_gemma":0.00001990089,"domain_scores_codex":[0.9993179,0.0000220463,0.000168972,0.0001474638,0.0002185016,0.000125131],"domain_scores_gemma":[0.9994179,0.00003129325,0.00004906819,0.0002400974,0.0002195145,0.00004212528],"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":[0.000006979326,0.00002252742,0.0003393804,0.00005000918,0.00003672523,0.000002684278,0.01094632,0.02481174,0.0008952548,0.7323886,0.003220978,0.2272788],"study_design_scores_gemma":[0.0001470591,0.00004584376,0.001306621,0.0000267505,0.000004647543,0.00002018621,0.0001957725,0.9867049,0.0005326328,0.001040679,0.009854983,0.000119894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01287683,0.0000672093,0.9798029,0.004261394,0.0001886627,0.0001968815,0.000003768121,0.000546655,0.002055684],"genre_scores_gemma":[0.9857897,0.000005060074,0.01343079,0.0005356194,0.00009403975,0.00005033637,0.0000419096,0.000005147886,0.00004744401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9729128,"threshold_uncertainty_score":0.6405368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278224826408542,"score_gpt":0.2756536029919525,"score_spread":0.2628713547278671,"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."}}