{"id":"W4226185317","doi":"10.1109/cogmi52975.2021.00010","title":"FireWarn: Fire Hazards Detection Using Deep Learning Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada; Queen's University","funders":"Defence Research and Development Canada","keywords":"Smoke; Fire detection; Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Bounding overwatch; Contextual image classification; Pattern recognition (psychology); Test set; Image (mathematics); Computer vision; Remote sensing; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006263079,0.001469887,0.0005989179,0.001009546,0.0003449009,0.000746035,0.001802854,0.001124866,0.003379997],"category_scores_gemma":[0.00120568,0.0006005163,0.0008817124,0.0005441097,0.0002758332,0.001327321,0.001005013,0.001554926,0.00136104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189712,"about_ca_system_score_gemma":0.001103088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01934516,"about_ca_topic_score_gemma":0.03358924,"domain_scores_codex":[0.9997004,0.00003303182,0.00001439526,0.0001112576,0.00007298766,0.00006794238],"domain_scores_gemma":[0.9997597,0.0000633283,0.0000295759,0.0000537053,0.00006908404,0.00002455726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001030356,0.0009055118,0.01782242,0.0004529617,0.0005202933,0.0003379041,0.00009783568,0.3360391,0.02455166,0.002616886,0.05253283,0.5630922],"study_design_scores_gemma":[0.000027585,0.00008921717,0.001732578,0.00002886541,0.00002819841,0.00005352736,0.00001656125,0.9844487,0.009712692,0.001189242,0.002655618,0.00001720287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3731099,0.003740047,0.5360866,0.001121444,0.0007640715,0.0005184285,0.0168638,0.05426976,0.01352592],"genre_scores_gemma":[0.7358651,0.0008602201,0.2194077,0.0006798266,0.0001219221,0.000245668,0.02842597,0.0005991429,0.01379447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01934516,"threshold_uncertainty_score":0.03846514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902053814283873,"score_gpt":0.205248325788981,"score_spread":0.1862277876461423,"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."}}