{"id":"W4320512333","doi":"10.2991/978-2-494069-31-2_229","title":"Bi-Model Helmet Wearing Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Correctness; Mainstream; Computer science; Visualization; Head (geology); Artificial intelligence; Human–computer interaction; Computer vision; Data science; Algorithm; Political science; Geology","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","sts","scholarly_communication","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.008293955,0.0005333461,0.0006502578,0.00646387,0.01176698,0.001616796,0.001533494,0.0003023322,0.0005454056],"category_scores_gemma":[0.0002828309,0.0006170479,0.0001027575,0.002657063,0.01485605,0.005249923,0.000668606,0.002805111,0.0000251975],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004513751,"about_ca_system_score_gemma":0.006427913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003967875,"about_ca_topic_score_gemma":0.003578086,"domain_scores_codex":[0.9915426,0.0003329342,0.000947131,0.001338701,0.003921977,0.001916605],"domain_scores_gemma":[0.9970899,0.0004446408,0.0002231665,0.0003901995,0.001568222,0.0002838873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002786792,0.0001399381,0.00009054651,0.0004042225,0.000005404636,0.000001345281,0.03045816,0.000052101,0.0001416289,0.7858058,0.0004559775,0.182417],"study_design_scores_gemma":[0.0002883115,0.0001393723,0.0004609084,0.0002497776,0.000006844321,0.000005733148,0.08556935,0.0006279689,0.00005519946,0.1373086,0.7745795,0.0007085224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00621671,0.02136757,0.00001389913,0.0001468168,0.002894174,0.001219972,0.0000429807,0.0001223512,0.9679755],"genre_scores_gemma":[0.6863171,0.04673343,0.0001125581,0.0001780034,0.004052628,0.0009194231,0.00006178256,0.0001432823,0.2614818],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7741235,"threshold_uncertainty_score":0.9996281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08004781807209678,"score_gpt":0.4117195468199734,"score_spread":0.3316717287478766,"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."}}