{"id":"W4384920015","doi":"10.23977/jaip.2023.060503","title":"Design study of fire risk early warning robot","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Warning system; Manual fire alarm activation; ALARM; Fire detection; Firefighting; Risk analysis (engineering); False alarm; Flexibility (engineering); Fire protection; Computer science; Engineering; Computer security; Forensic engineering; Artificial intelligence; Architectural engineering; Business; Civil engineering; Telecommunications; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008266388,0.000782032,0.0006522916,0.0004898354,0.0008325629,0.000882709,0.001621988,0.001133086,0.005134286],"category_scores_gemma":[0.0009308144,0.000393185,0.0005433836,0.0001627195,0.0005045944,0.0008259923,0.0007539928,0.000464751,0.0009746031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004008349,"about_ca_system_score_gemma":0.001198624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002275308,"about_ca_topic_score_gemma":0.001083625,"domain_scores_codex":[0.9992976,0.0001440872,0.00004067812,0.0001746081,0.0002637525,0.00007926056],"domain_scores_gemma":[0.999519,0.0000961966,0.00007127574,0.00004537073,0.0002127231,0.0000555203],"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":[0.001014183,0.0005268813,0.008188304,0.002880921,0.0002076526,0.002430359,0.001759061,0.3946987,0.2357598,0.04372212,0.004917588,0.3038945],"study_design_scores_gemma":[0.0002395549,0.003278487,0.00358111,0.000141241,0.0001994743,0.00118966,0.0003311849,0.9184247,0.03652827,0.005445589,0.03052595,0.0001148746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03198974,0.000506133,0.9525543,0.0003012793,0.0001587994,0.0005595056,0.00004982046,0.001033273,0.01284705],"genre_scores_gemma":[0.7582132,0.0006876402,0.2207389,0.0001789543,0.0000660303,0.001439873,0.0001358554,0.00007613839,0.01846352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005134286,"threshold_uncertainty_score":0.01717591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07059250718815392,"score_gpt":0.3128265366112489,"score_spread":0.242234029423095,"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."}}