{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002422614,0.0001235118,0.0002761095,0.0002382587,0.000103512,0.00006048629,0.0002099001,0.00007018861,0.00003608119],"category_scores_gemma":[0.001739313,0.0001142313,0.00008904546,0.0007923593,0.00002113617,0.0006026762,0.00002132534,0.0005418999,0.0001935603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004932297,"about_ca_system_score_gemma":0.00003084999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001720912,"about_ca_topic_score_gemma":0.00001786263,"domain_scores_codex":[0.997923,0.0003696333,0.0009801507,0.0001000959,0.000438915,0.0001882577],"domain_scores_gemma":[0.9976541,0.001143082,0.0005957747,0.000166811,0.0003573523,0.0000828738],"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":[0.0002568121,0.0001850007,0.0001928765,0.00002147655,0.000203142,0.0001064921,0.01448128,0.8734533,0.006651741,0.00001851744,0.000239364,0.1041901],"study_design_scores_gemma":[0.000197879,0.003401186,0.003135711,0.0001687493,0.0003280918,0.0002819921,0.07897019,0.8633679,0.04578429,0.000571905,0.003362581,0.0004295254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7123527,0.0001347973,0.2848862,0.0001256801,0.001920742,0.0002295422,0.000001120892,0.0001174192,0.0002318547],"genre_scores_gemma":[0.9976874,0.0002455297,0.001756704,0.000007124094,0.0002346153,0.000003405227,1.339394e-7,0.00002524285,0.00003983499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2853347,"threshold_uncertainty_score":0.4658215,"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."}}