{"id":"W4285101200","doi":"10.1109/aiiot54504.2022.9817232","title":"Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"NIST; Support vector machine; Logistic regression; Computer science; Artificial intelligence; Machine learning; Work (physics); Regression analysis; Fire safety; Training (meteorology); Aeronautics; Environmental science; Forensic engineering; Statistics; Engineering; Meteorology; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001837672,0.0009587791,0.0005520212,0.001754828,0.0002725626,0.0009947921,0.0005641833,0.0007829816,0.0007398722],"category_scores_gemma":[0.005890884,0.0002617977,0.0007426805,0.001013257,0.0002529711,0.001199715,0.0003903958,0.0008373074,0.0005290519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445755,"about_ca_system_score_gemma":0.0005748763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009094297,"about_ca_topic_score_gemma":0.00768224,"domain_scores_codex":[0.9991624,0.0003022462,0.00007334741,0.0001678414,0.0002115795,0.00008254594],"domain_scores_gemma":[0.996986,0.001918715,0.0003769603,0.0001919537,0.0004613398,0.00006500156],"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":[0.0003473185,0.0006966229,0.1756233,0.0001043472,0.0002777185,0.000167699,0.0002212789,0.5697105,0.007348499,0.0008550241,0.001077941,0.2435697],"study_design_scores_gemma":[0.000004982996,0.00008513446,0.01114791,0.00001083966,0.00001479156,0.0000276369,0.00008651135,0.9860564,0.002012167,0.0003807011,0.000157631,0.00001524672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8151076,0.0003554611,0.1813601,0.0002367755,0.00006755635,0.00007701005,0.0002897908,0.0008410552,0.001664724],"genre_scores_gemma":[0.963649,0.0001169846,0.03495471,0.00002532851,0.00001609772,0.00003663002,0.0003397538,0.00002135995,0.0008400358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009094297,"threshold_uncertainty_score":0.01808274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232803376877445,"score_gpt":0.2698443647249787,"score_spread":0.2465640270372342,"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."}}