{"id":"W3105393511","doi":"10.1007/s11042-021-11766-3","title":"STCNet: spatiotemporal cross network for industrial smoke detection","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Jiangsu Provincial Key Research and Development Program; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Smoke; Artificial intelligence; Motion (physics); Path (computing); Code (set theory); Task (project management); Computer 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":[0.0001732998,0.00009581722,0.0001130069,0.00003567279,0.0004866853,0.0000616902,0.00007643866,0.00006782525,0.00005432204],"category_scores_gemma":[0.00001252269,0.0001083565,0.00004626832,0.0001898477,0.00002365677,0.00007050137,0.00002618507,0.000169937,0.000007143738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005721997,"about_ca_system_score_gemma":0.00001117415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004549305,"about_ca_topic_score_gemma":0.00003866128,"domain_scores_codex":[0.9993356,0.00001858123,0.0002153571,0.0001607946,0.00009795549,0.0001717027],"domain_scores_gemma":[0.9996274,0.00009841243,0.00003979315,0.0001454183,0.00002462672,0.00006436955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006098029,0.00004686961,0.002855993,0.00005254321,0.00006290626,4.415836e-7,0.0002773108,0.1108632,0.002123128,0.0006418089,0.00512312,0.8778917],"study_design_scores_gemma":[0.0008872633,0.00005357717,0.005615539,0.000002764069,0.00001214263,0.000006568194,0.00008528425,0.25254,0.0006703288,0.0002291406,0.7397045,0.0001928484],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3480809,0.00121899,0.6279485,0.000611425,0.00553072,0.008721679,0.002884149,0.002145942,0.002857789],"genre_scores_gemma":[0.9933999,0.00001284274,0.0008334019,0.00004220087,0.001087763,0.004200755,0.0001871594,0.0000282009,0.0002078469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8776988,"threshold_uncertainty_score":0.4418651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04146220603052695,"score_gpt":0.2531490601909085,"score_spread":0.2116868541603815,"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."}}