{"id":"W4364322545","doi":"10.1109/tai.2023.3266183","title":"Visual Relationship Detection for Workplace Safety Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Bayesian network; Context (archaeology); Object detection; Visualization; Artificial neural network; Artificial intelligence; Object (grammar); Graphical user interface; Machine learning; Data mining; Pattern recognition (psychology); Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001357548,0.001054026,0.0004713266,0.001405807,0.0004419376,0.001267279,0.002095411,0.001850874,0.01254907],"category_scores_gemma":[0.007966897,0.0003876494,0.0006284679,0.0006326187,0.0003035302,0.001830579,0.00181374,0.001098871,0.004873875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009069036,"about_ca_system_score_gemma":0.0008043231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005433299,"about_ca_topic_score_gemma":0.008523021,"domain_scores_codex":[0.9988009,0.0002039789,0.00004942338,0.0003755729,0.0004721397,0.00009803091],"domain_scores_gemma":[0.9981281,0.0005818828,0.0001466715,0.0002845835,0.0007478659,0.0001109167],"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":[0.0009421234,0.0005965217,0.008517876,0.0004825878,0.00009922912,0.000319352,0.0002048177,0.03259439,0.03928344,0.002977534,0.03628899,0.8776932],"study_design_scores_gemma":[0.0000762973,0.0004386752,0.008535148,0.0001509219,0.00006830465,0.0004598184,0.0002199812,0.8804022,0.06550353,0.02065418,0.02341805,0.00007296047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0724835,0.001387818,0.8603871,0.001747716,0.0003561691,0.000513254,0.003219207,0.04571884,0.01418626],"genre_scores_gemma":[0.5580574,0.0005540543,0.4257096,0.0008682318,0.0001129293,0.0003220178,0.00497503,0.0007196627,0.008681071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01254907,"threshold_uncertainty_score":0.0419808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05401277960001292,"score_gpt":0.34603996480937,"score_spread":0.2920271852093571,"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."}}