{"id":"W4200439538","doi":"10.1155/2021/7534739","title":"YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Shanghai Science and Technology Development Foundation","keywords":"Crew; Rope; Warning system; Supervisor; Computer science; Simulation; Real-time computing; Engineering; Aeronautics; Structural engineering","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":[0.00015813,0.00009425341,0.0001911657,0.00007264544,0.00005536393,0.00001827532,0.00003250861,0.00006671483,0.00004146987],"category_scores_gemma":[0.00001718138,0.00009814557,0.00006939846,0.0001808014,0.00001281585,0.0003946222,9.078418e-7,0.0002142912,0.000001874822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004306039,"about_ca_system_score_gemma":0.00002051936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003674249,"about_ca_topic_score_gemma":0.000008115936,"domain_scores_codex":[0.9991758,0.00002553902,0.0004326295,0.00008958398,0.0001710092,0.0001054225],"domain_scores_gemma":[0.9995453,0.00003797317,0.0001209936,0.00005987739,0.0001589468,0.00007684853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002952921,0.00005658148,0.001954783,0.0002568086,0.0001214321,0.00008347299,0.004128602,0.4472135,0.4004211,0.0002365809,0.00001587572,0.145216],"study_design_scores_gemma":[0.002856731,0.0001758674,0.9539153,0.0001955571,0.0001395,0.0001675229,0.0008359015,0.01065449,0.02659674,0.0005151863,0.003590743,0.0003564828],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9389707,0.0003562846,0.05814865,0.00003657006,0.0001808724,0.00007020737,0.000006105877,0.00006433715,0.002166215],"genre_scores_gemma":[0.9818046,0.000847351,0.01713218,0.000008847036,0.00007458679,0.000001949134,0.00002268595,0.00001896972,0.00008882152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9519605,"threshold_uncertainty_score":0.400226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00469839569874543,"score_gpt":0.2032761528016369,"score_spread":0.1985777571028915,"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."}}