{"id":"W4212958975","doi":"10.1109/access.2022.3151660","title":"Convolution Optimization in Fire Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Convolution (computer science); Block (permutation group theory); Artificial intelligence; Deep learning; Computer engineering; FLOPS; Computation; Residual; Machine learning; Algorithm; Parallel computing; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001044926,0.00004973224,0.00006023902,0.00008191699,0.00007192235,0.00002670276,0.0001180985,0.00002675728,0.0001656954],"category_scores_gemma":[0.000004828272,0.00006089654,0.00001638426,0.0003381987,0.000005153614,0.0001981158,0.0000128924,0.0001049424,0.00001270584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001869462,"about_ca_system_score_gemma":0.000007637004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005877877,"about_ca_topic_score_gemma":0.00002081517,"domain_scores_codex":[0.9995378,0.00003648955,0.0001479544,0.00008741832,0.0001051735,0.00008516662],"domain_scores_gemma":[0.9998346,0.00001126287,0.00002317056,0.0001018155,0.00001243761,0.00001669339],"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":[0.000004571256,0.000008525941,0.0008902572,0.00001339844,0.000002505273,0.000001052314,0.00009663893,0.994285,0.0005976287,0.00003250924,0.001422592,0.002645303],"study_design_scores_gemma":[0.0001688022,0.000007301112,0.007582762,0.000003629125,0.000001315581,0.000004098578,0.0000699829,0.9869592,0.0002542445,0.00001401177,0.004862572,0.00007205646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8404208,0.0003252959,0.1401122,0.0003349065,0.00640277,0.0006164053,0.00002457717,0.0009376958,0.01082534],"genre_scores_gemma":[0.9996082,0.00001970076,0.00003579975,0.00003278672,0.00004900752,0.0001137174,0.00002033638,0.00001256161,0.0001078477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1591874,"threshold_uncertainty_score":0.2483289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834709714939807,"score_gpt":0.2493525475481674,"score_spread":0.2210054503987694,"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."}}