{"id":"W1529665683","doi":"","title":"Sensor, Filter, and Fusion Models with Rough Petri Nets","year":2001,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Petri net; Context (archaeology); Rough set; Relevance (law); Sensor fusion; Filter (signal processing); Process architecture; Stochastic Petri net; Computer science; Fusion rules; Wireless sensor network; Theoretical computer science; Artificial intelligence; Algorithm; Data mining; Computer vision","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":[0.0003190434,0.000224529,0.0002454006,0.0001876857,0.0002153822,0.0004826458,0.000603644,0.00006793391,0.00003037388],"category_scores_gemma":[0.00001462654,0.0001767422,0.00003469118,0.0004795802,0.00005572482,0.002864208,0.0004613161,0.0001556981,0.000107819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008594075,"about_ca_system_score_gemma":0.00004802157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003978712,"about_ca_topic_score_gemma":0.000005749607,"domain_scores_codex":[0.9982305,0.0000366517,0.0004737959,0.0002671481,0.0005507552,0.0004411504],"domain_scores_gemma":[0.9987732,0.00009133188,0.0001711312,0.0007100866,0.0000750619,0.0001792153],"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":[0.0005274991,0.0007575084,0.01444974,0.001159481,0.0005066221,0.0006155114,0.1256261,0.1788413,0.0008490559,0.1562166,0.01901379,0.5014367],"study_design_scores_gemma":[0.0009154625,0.0001781303,0.0001969352,0.0001463988,0.000007946913,0.0005686867,0.0002992575,0.970592,0.00008027349,0.0008114141,0.02589046,0.0003130523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2202207,0.0001536779,0.7470119,0.0005561155,0.0002235432,0.0003331067,0.000004867087,0.0002566577,0.03123946],"genre_scores_gemma":[0.8520858,0.0001434317,0.1455874,0.001403587,0.00004993636,0.000025316,0.000007824065,0.00001520108,0.0006815672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7917507,"threshold_uncertainty_score":0.7207336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795754249442898,"score_gpt":0.2407896860156395,"score_spread":0.2028321435212106,"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."}}